WO2024032809A1 - 生命信息处理系统和生命信息处理方法 - Google Patents
生命信息处理系统和生命信息处理方法 Download PDFInfo
- Publication number
- WO2024032809A1 WO2024032809A1 PCT/CN2023/112988 CN2023112988W WO2024032809A1 WO 2024032809 A1 WO2024032809 A1 WO 2024032809A1 CN 2023112988 W CN2023112988 W CN 2023112988W WO 2024032809 A1 WO2024032809 A1 WO 2024032809A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- patient
- data
- status
- change trend
- trend
- Prior art date
Links
- 230000010365 information processing Effects 0.000 title claims abstract description 113
- 238000003672 processing method Methods 0.000 title claims abstract description 17
- 230000008859 change Effects 0.000 claims abstract description 222
- 238000004458 analytical method Methods 0.000 claims abstract description 104
- 210000002216 heart Anatomy 0.000 claims description 71
- 238000012544 monitoring process Methods 0.000 claims description 67
- 210000004369 blood Anatomy 0.000 claims description 59
- 239000008280 blood Substances 0.000 claims description 59
- 238000000034 method Methods 0.000 claims description 56
- 238000010801 machine learning Methods 0.000 claims description 45
- 210000000056 organ Anatomy 0.000 claims description 40
- 210000002345 respiratory system Anatomy 0.000 claims description 40
- 210000000653 nervous system Anatomy 0.000 claims description 33
- 238000012545 processing Methods 0.000 claims description 27
- 238000012360 testing method Methods 0.000 claims description 26
- 230000036772 blood pressure Effects 0.000 claims description 25
- 210000001519 tissue Anatomy 0.000 claims description 21
- 238000011282 treatment Methods 0.000 claims description 20
- 230000000004 hemodynamic effect Effects 0.000 claims description 18
- 230000002159 abnormal effect Effects 0.000 claims description 16
- 238000006213 oxygenation reaction Methods 0.000 claims description 15
- 230000010412 perfusion Effects 0.000 claims description 15
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 14
- 230000029058 respiratory gaseous exchange Effects 0.000 claims description 12
- 210000003792 cranial nerve Anatomy 0.000 claims description 11
- 230000000747 cardiac effect Effects 0.000 claims description 10
- 238000003745 diagnosis Methods 0.000 claims description 9
- 238000007689 inspection Methods 0.000 claims description 8
- 230000036760 body temperature Effects 0.000 claims description 7
- 210000004556 brain Anatomy 0.000 claims description 7
- 239000001569 carbon dioxide Substances 0.000 claims description 7
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 7
- 201000010099 disease Diseases 0.000 claims description 7
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 7
- 206010002091 Anaesthesia Diseases 0.000 claims description 6
- 230000037005 anaesthesia Effects 0.000 claims description 6
- 238000001802 infusion Methods 0.000 claims description 6
- 210000004185 liver Anatomy 0.000 claims description 6
- 238000009666 routine test Methods 0.000 claims description 6
- 238000003384 imaging method Methods 0.000 claims description 5
- 210000003734 kidney Anatomy 0.000 claims description 5
- 230000000241 respiratory effect Effects 0.000 claims description 5
- 230000015271 coagulation Effects 0.000 claims description 4
- 238000005345 coagulation Methods 0.000 claims description 4
- 210000002249 digestive system Anatomy 0.000 claims description 4
- 210000004072 lung Anatomy 0.000 claims description 4
- 230000000474 nursing effect Effects 0.000 claims description 4
- 210000002784 stomach Anatomy 0.000 claims description 4
- 230000002485 urinary effect Effects 0.000 claims description 4
- 238000010876 biochemical test Methods 0.000 claims description 3
- 238000009535 clinical urine test Methods 0.000 claims description 3
- 210000000750 endocrine system Anatomy 0.000 claims description 3
- 239000007789 gas Substances 0.000 claims description 3
- 230000003907 kidney function Effects 0.000 claims description 3
- 238000007449 liver function test Methods 0.000 claims description 3
- 230000004089 microcirculation Effects 0.000 claims description 3
- 230000008557 oxygen metabolism Effects 0.000 claims description 3
- 210000004994 reproductive system Anatomy 0.000 claims description 3
- 210000001685 thyroid gland Anatomy 0.000 claims description 3
- 238000012285 ultrasound imaging Methods 0.000 claims description 3
- 230000004962 physiological condition Effects 0.000 claims description 2
- 238000002106 pulse oximetry Methods 0.000 claims description 2
- 230000002123 temporal effect Effects 0.000 claims description 2
- 238000012879 PET imaging Methods 0.000 claims 2
- 238000013170 computed tomography imaging Methods 0.000 claims 2
- 239000000439 tumor marker Substances 0.000 claims 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 49
- 229910052760 oxygen Inorganic materials 0.000 description 49
- 239000001301 oxygen Substances 0.000 description 49
- 238000010586 diagram Methods 0.000 description 19
- 238000004891 communication Methods 0.000 description 17
- 230000006866 deterioration Effects 0.000 description 13
- 230000004087 circulation Effects 0.000 description 12
- 239000012530 fluid Substances 0.000 description 10
- 238000011156 evaluation Methods 0.000 description 9
- 206010019280 Heart failures Diseases 0.000 description 8
- 238000013480 data collection Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 8
- 238000005259 measurement Methods 0.000 description 8
- 206010003658 Atrial Fibrillation Diseases 0.000 description 7
- 208000024172 Cardiovascular disease Diseases 0.000 description 7
- 238000011161 development Methods 0.000 description 7
- 239000007788 liquid Substances 0.000 description 6
- 230000005856 abnormality Effects 0.000 description 5
- 230000007423 decrease Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000005070 sampling Methods 0.000 description 5
- 238000012549 training Methods 0.000 description 5
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 4
- 206010003119 arrhythmia Diseases 0.000 description 4
- 230000006793 arrhythmia Effects 0.000 description 4
- 239000003814 drug Substances 0.000 description 4
- 229940079593 drug Drugs 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000003862 health status Effects 0.000 description 4
- 208000015181 infectious disease Diseases 0.000 description 4
- 230000036387 respiratory rate Effects 0.000 description 4
- 210000002700 urine Anatomy 0.000 description 4
- 238000009423 ventilation Methods 0.000 description 4
- 206010001052 Acute respiratory distress syndrome Diseases 0.000 description 3
- 208000004756 Respiratory Insufficiency Diseases 0.000 description 3
- 206010040047 Sepsis Diseases 0.000 description 3
- 208000027418 Wounds and injury Diseases 0.000 description 3
- 210000001015 abdomen Anatomy 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000006378 damage Effects 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 3
- 210000003743 erythrocyte Anatomy 0.000 description 3
- 210000003128 head Anatomy 0.000 description 3
- 208000014674 injury Diseases 0.000 description 3
- 230000003908 liver function Effects 0.000 description 3
- 238000010295 mobile communication Methods 0.000 description 3
- 210000003205 muscle Anatomy 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 230000009469 supplementation Effects 0.000 description 3
- 230000008685 targeting Effects 0.000 description 3
- 206010047302 ventricular tachycardia Diseases 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 208000009304 Acute Kidney Injury Diseases 0.000 description 2
- 102100036475 Alanine aminotransferase 1 Human genes 0.000 description 2
- 108010082126 Alanine transaminase Proteins 0.000 description 2
- 101800000407 Brain natriuretic peptide 32 Proteins 0.000 description 2
- 102400000667 Brain natriuretic peptide 32 Human genes 0.000 description 2
- 101800002247 Brain natriuretic peptide 45 Proteins 0.000 description 2
- 108020004206 Gamma-glutamyltransferase Proteins 0.000 description 2
- 208000032843 Hemorrhage Diseases 0.000 description 2
- 206010058558 Hypoperfusion Diseases 0.000 description 2
- 208000033626 Renal failure acute Diseases 0.000 description 2
- 208000013616 Respiratory Distress Syndrome Diseases 0.000 description 2
- 201000011040 acute kidney failure Diseases 0.000 description 2
- 201000000028 adult respiratory distress syndrome Diseases 0.000 description 2
- 229910021529 ammonia Inorganic materials 0.000 description 2
- 230000000740 bleeding effect Effects 0.000 description 2
- 230000001364 causal effect Effects 0.000 description 2
- 210000000038 chest Anatomy 0.000 description 2
- 238000003759 clinical diagnosis Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 230000001934 delay Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000002059 diagnostic imaging Methods 0.000 description 2
- 210000000981 epithelium Anatomy 0.000 description 2
- 102000006640 gamma-Glutamyltransferase Human genes 0.000 description 2
- 238000004868 gas analysis Methods 0.000 description 2
- 238000007917 intracranial administration Methods 0.000 description 2
- JVTAAEKCZFNVCJ-UHFFFAOYSA-N lactic acid Chemical compound CC(O)C(O)=O JVTAAEKCZFNVCJ-UHFFFAOYSA-N 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- HPNRHPKXQZSDFX-OAQDCNSJSA-N nesiritide Chemical compound C([C@H]1C(=O)NCC(=O)N[C@@H](CCCNC(N)=N)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CC(O)=O)C(=O)N[C@@H](CCCNC(N)=N)C(=O)N[C@H](C(N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](CSSC[C@@H](C(=O)N1)NC(=O)CNC(=O)[C@H](CO)NC(=O)CNC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](CCSC)NC(=O)[C@H](CCCCN)NC(=O)[C@H]1N(CCC1)C(=O)[C@@H](N)CO)C(C)C)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(N)=N)C(=O)N[C@@H](CCCNC(N)=N)C(=O)N[C@@H](CC=1N=CNC=1)C(O)=O)=O)[C@@H](C)CC)C1=CC=CC=C1 HPNRHPKXQZSDFX-OAQDCNSJSA-N 0.000 description 2
- 230000035790 physiological processes and functions Effects 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000007639 printing Methods 0.000 description 2
- 238000012797 qualification Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 201000004193 respiratory failure Diseases 0.000 description 2
- 238000012502 risk assessment Methods 0.000 description 2
- 230000035939 shock Effects 0.000 description 2
- 230000009885 systemic effect Effects 0.000 description 2
- 238000002560 therapeutic procedure Methods 0.000 description 2
- 238000002604 ultrasonography Methods 0.000 description 2
- 208000004476 Acute Coronary Syndrome Diseases 0.000 description 1
- BVKZGUZCCUSVTD-UHFFFAOYSA-M Bicarbonate Chemical compound OC([O-])=O BVKZGUZCCUSVTD-UHFFFAOYSA-M 0.000 description 1
- 206010008111 Cerebral haemorrhage Diseases 0.000 description 1
- 206010010071 Coma Diseases 0.000 description 1
- 102000004420 Creatine Kinase Human genes 0.000 description 1
- 108010042126 Creatine kinase Proteins 0.000 description 1
- 208000028399 Critical Illness Diseases 0.000 description 1
- 238000008789 Direct Bilirubin Methods 0.000 description 1
- 206010019196 Head injury Diseases 0.000 description 1
- 102000001554 Hemoglobins Human genes 0.000 description 1
- 108010054147 Hemoglobins Proteins 0.000 description 1
- 206010021143 Hypoxia Diseases 0.000 description 1
- 108010044467 Isoenzymes Proteins 0.000 description 1
- JVTAAEKCZFNVCJ-UHFFFAOYSA-M Lactate Chemical compound CC(O)C([O-])=O JVTAAEKCZFNVCJ-UHFFFAOYSA-M 0.000 description 1
- 102400001263 NT-proBNP Human genes 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- 208000001647 Renal Insufficiency Diseases 0.000 description 1
- 206010038678 Respiratory depression Diseases 0.000 description 1
- 208000001871 Tachycardia Diseases 0.000 description 1
- RTAQQCXQSZGOHL-UHFFFAOYSA-N Titanium Chemical compound [Ti] RTAQQCXQSZGOHL-UHFFFAOYSA-N 0.000 description 1
- 238000008050 Total Bilirubin Reagent Methods 0.000 description 1
- 208000003443 Unconsciousness Diseases 0.000 description 1
- 206010047700 Vomiting Diseases 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 229940035676 analgesics Drugs 0.000 description 1
- 239000000730 antalgic agent Substances 0.000 description 1
- 239000002220 antihypertensive agent Substances 0.000 description 1
- 229940127088 antihypertensive drug Drugs 0.000 description 1
- 230000004872 arterial blood pressure Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 208000034158 bleeding Diseases 0.000 description 1
- 238000004820 blood count Methods 0.000 description 1
- 230000006931 brain damage Effects 0.000 description 1
- 231100000874 brain damage Toxicity 0.000 description 1
- 208000029028 brain injury Diseases 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 208000026106 cerebrovascular disease Diseases 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 239000000084 colloidal system Substances 0.000 description 1
- 230000018044 dehydration Effects 0.000 description 1
- 238000006297 dehydration reaction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000002542 deteriorative effect Effects 0.000 description 1
- 230000035487 diastolic blood pressure Effects 0.000 description 1
- 235000005911 diet Nutrition 0.000 description 1
- 230000000378 dietary effect Effects 0.000 description 1
- 239000002934 diuretic Substances 0.000 description 1
- 230000002526 effect on cardiovascular system Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000029142 excretion Effects 0.000 description 1
- 238000002618 extracorporeal membrane oxygenation Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000002637 fluid replacement therapy Methods 0.000 description 1
- 208000018875 hypoxemia Diseases 0.000 description 1
- 210000000987 immune system Anatomy 0.000 description 1
- 238000000338 in vitro Methods 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 201000006370 kidney failure Diseases 0.000 description 1
- 238000009533 lab test Methods 0.000 description 1
- 239000004310 lactic acid Substances 0.000 description 1
- 235000014655 lactic acid Nutrition 0.000 description 1
- 230000004446 light reflex Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 206010025482 malaise Diseases 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000002483 medication Methods 0.000 description 1
- 230000006996 mental state Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 208000010125 myocardial infarction Diseases 0.000 description 1
- 230000001452 natriuretic effect Effects 0.000 description 1
- 238000003058 natural language processing Methods 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 238000002640 oxygen therapy Methods 0.000 description 1
- 230000036407 pain Effects 0.000 description 1
- 230000036513 peripheral conductance Effects 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 210000004180 plasmocyte Anatomy 0.000 description 1
- 108010008064 pro-brain natriuretic peptide (1-76) Proteins 0.000 description 1
- 230000002685 pulmonary effect Effects 0.000 description 1
- 210000001747 pupil Anatomy 0.000 description 1
- 230000001179 pupillary effect Effects 0.000 description 1
- 238000010992 reflux Methods 0.000 description 1
- 238000002644 respiratory therapy Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000035900 sweating Effects 0.000 description 1
- 238000002636 symptomatic treatment Methods 0.000 description 1
- 230000035488 systolic blood pressure Effects 0.000 description 1
- 230000006794 tachycardia Effects 0.000 description 1
- 230000002537 thrombolytic effect Effects 0.000 description 1
- 229910052719 titanium Inorganic materials 0.000 description 1
- 239000010936 titanium Substances 0.000 description 1
- 238000002627 tracheal intubation Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 230000002227 vasoactive effect Effects 0.000 description 1
- 230000003519 ventilatory effect Effects 0.000 description 1
- 230000002861 ventricular Effects 0.000 description 1
- 230000008673 vomiting Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Definitions
- This application relates to the field of life information technology, and more specifically to a life information processing system and a life information processing method.
- the first aspect of the embodiments of the present application provides a life information processing system.
- the life information processing system includes a memory, a processor, and a display.
- the memory is used to store executable programs, and the processor is used to execute the executable program. Executing a program causes the processor to:
- the patient data is analyzed to obtain a plurality of analysis results, wherein the plurality of analysis results at least include a first change trend of the first information extracted from the patient data and a second change trend of the second information. ;
- the patient status of the patient related to the preset condition is determined, and the patient status at least includes the patient's physiological condition.
- the physiological system includes at least one of the patient's nervous system, circulatory system, and respiratory system;
- the display is controlled to display patient status prompt information characterizing the patient's status, and to display the first change trend and the second change trend, wherein the patient status prompt information at least includes information on the status of the physiological system. overall assessment, and where,
- At least one of the first change trend and the second change trend includes an indicator related to cranial nerves
- At least one of the first change trend and the second change trend includes an indicator related to hemodynamics or perfusion
- At least one of the first change trend and the second change trend includes an oxygenation-related indicator.
- the second aspect of the embodiments of the present application provides a method for processing life information.
- the method includes:
- the patient data is analyzed to obtain a plurality of analysis results, wherein the plurality of analysis results at least include a first change trend of the first information extracted from the patient data and a second change trend of the second information.
- the first information and the second information are related to the same patient state of the patient, and the patient state at least includes the state of the patient's physiological system;
- the first change trend and the second change trend are displayed.
- the third aspect of the embodiments of the present application provides a method for processing life information.
- the method includes:
- the patient data is analyzed to obtain a plurality of analysis results, wherein the plurality of analysis results at least include a first change trend of the first information extracted from the patient data and a second change trend of the second information. ;
- the life information processing system and life information processing method comprehensively analyze multiple types of patient data to obtain patient status, fully consider the correlation between multiple types of patient data, and can avoid false alarms and missing information. alarm and other issues, and can help medical staff quickly and reliably judge the patient's condition, which is conducive to real-time control of the patient's health status; in the process of determining the patient's status, the correlation of various changing trends of the patient's data is taken into account, which can improve Accuracy of patient status judgment.
- Figure 1 shows a schematic block diagram of a life information processing system according to an embodiment of the present application
- Figure 2 shows a schematic diagram of a display interface according to an embodiment of the present application
- Figure 3 shows a schematic flow chart of a life information processing method according to an embodiment of the present application
- Figure 4 shows a schematic flow chart of a life information processing method according to another embodiment of the present application.
- Figure 5 shows a schematic diagram of a display interface according to another embodiment of the present application.
- Figure 6 shows a schematic diagram of a display interface according to yet another embodiment of the present application.
- display interface can be the interface on which the life information processing system displays parameter waveforms and/or parameter values; or it can be the interface displayed after the life information processing system is turned on; or it can be the interface that the life information processing system uses more frequently. interface.
- monitoring equipment mainly uses the following alarm methods to alarm:
- the alarm method is based on a single vital sign data. When it is determined that the value of a single vital sign data exceeds the alarm threshold at a preset time point or within a preset period of time, an alarm is issued. ;
- the second is an alarm method based on multiple vital sign data. When multiple vital sign data are determined to exceed the alarm threshold at the same time, an alarm is issued;
- the third is a trend alarm method based on a single vital sign data. When a single vital sign data is determined to be in the preset segment An alarm will be issued when there is a downward or upward trend within a certain period of time.
- embodiments of the present application provide a life information processing system for realizing status monitoring.
- Status monitoring refers to monitoring the patient's status and providing prompts or alarms when abnormal patient status is detected.
- Patient status is the evaluation result of the patient's overall or partial physiological function, reflecting the patient's overall or partial health status.
- the vital information processing system of the embodiment of the present application takes into account the correlation between multiple changing trends of the patient data, thereby improving the accuracy of the patient data judgment.
- the life information processing system 100 of the embodiment of the present application includes a memory 110, a processor 120 and a display 130.
- the memory 110 is used to store executable programs
- the processor 120 is used to execute the executable programs stored in the memory 110.
- the processor 120 is caused to perform the following operations: obtain multiple types of patient data of the patient; analyze the patient data to obtain multiple analysis results, wherein the multiple analysis results include at least the first information extracted from the patient data. A change trend and a second change trend of the second information; judging whether the first change trend and the second change trend meet the preset conditions related to the same patient status; when judging that the first change trend and the second change trend meet the preset conditions , the first change trend and the second change trend are displayed.
- the life information processing system of the embodiment of the present application comprehensively analyzes multiple types of patient data to obtain the patient's status and the physiological reasons causing the patient's status, and prompts the patient's status and physiological reasons, and provides a monitoring plan based on parameter values or parameter trends.
- monitoring solutions based on patient status fully consider the correlation between various patient data, which can avoid problems such as false alarms and missed alarms in the life information processing system, and medical staff can quickly and reliably judge based on patient status information.
- the patient's condition is conducive to real-time control of the patient's health status.
- the life information processing system 100 in the embodiment of the present application includes, but is not limited to, any one of a monitor, a local central station, a remote central station, a cloud service system, a mobile terminal, or a combination thereof. life letter
- the information processing system 100 may be a portable life information processing system, a transit life information processing system, a mobile life information processing system, or the like.
- the life information processing system 100 may be a monitor, which is used to monitor patient monitoring parameters in real time.
- the monitor may include a bedside monitor, a wearable monitor, etc.
- Monitors can include ventilator monitors, anesthesia monitors, defibrillator monitors, intracranial pressure monitors, ECG monitors, etc.
- the life information processing system 100 may also include a central station for receiving monitoring data sent by the monitor and performing centralized monitoring of the monitoring data.
- the central station may include a local central station or a remote central station.
- the central station connects the monitors in one department or multiple departments through the network to achieve the purpose of real-time centralized monitoring and massive data storage.
- the central station stores monitoring data, basic patient information, medical history information, diagnostic information, etc., but is not limited to this.
- the monitor and the central station can form an interconnection platform through BeneLink to realize data communication between the monitor and the central station.
- the central station can access monitoring data monitored by the monitor.
- the monitor and the central station can also establish a data connection through a communication unit, including but not limited to Wifi, Bluetooth or 2G, 3G, 4G, 5G and other communication units of mobile communication.
- the life information processing system 100 in the embodiment of the present application can also be other devices besides monitoring equipment, such as image acquisition equipment, treatment and support equipment, information systems (such as CIS, HIS, shift software, decision support systems, etc.), mobile terminals (e.g. car inspection) etc.
- monitoring equipment such as image acquisition equipment, treatment and support equipment, information systems (such as CIS, HIS, shift software, decision support systems, etc.), mobile terminals (e.g. car inspection) etc.
- the processor 120 of the life information processing system 100 can be a central processing unit (Central Processing Unit, CPU), or other general-purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit) , ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
- a general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.
- the processor 120 is the control center of the life information processing system 100 and uses various interfaces and lines to connect various parts of the entire life information processing system 100 .
- the memory 110 of the vital information processing system 100 is used to store executable programs. Further, the memory 110 can also store patient data such as vital sign data of the patient associated with the vital information processing system 100 .
- the memory 110 may mainly include a program storage area and a data storage area, where the program storage area may store an operating system, application programs required for multiple functions, and the like.
- the memory 110 may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card, secure digital card, flash memory card, multiple disk storage devices, flash memory devices, or Other volatile solid-state storage devices.
- the display 130 is used to provide visual display output for the user.
- the display 130 can be used to provide a visual display interface for the user, including but not limited to a monitoring interface, a monitoring parameter setting interface, etc.
- the display 130 may be implemented as a touch display, or the display 130 may have an input panel, that is, the display 130 may serve as an input/output device.
- the life information processing system 100 further includes a data collection unit, such as a sensor.
- Sensors can be used to continuously collect patient monitoring data.
- continuous collection means that the sensor continuously measures monitoring data multiple times at a preset time, where the preset time refers to the shortest time for the sensor to return a piece of monitoring data.
- the data collection unit and the processor 120 can be connected through a wired communication protocol or a wireless communication protocol, so that data interaction can be performed between the data collection unit and the processor 120 .
- Wireless communication technologies include but are not limited to: various generations of mobile communication technologies (2G, 3G, 4G and 5G), wireless networks, Bluetooth, ZigBee, ultra-wideband UWB, NFC, etc.
- the data collection unit is used to collect the patient's vital sign data.
- the data collection unit may be independently provided outside the life information processing system 100 and detachably connected to the life information processing system 100 .
- the processor 120 is also used to perform data processing on the vital sign data from the data collection unit.
- the life information processing system 100 may not include sensors, and the life information processing system 100 may receive monitoring data collected by external monitoring accessories through a communication unit.
- the life information processing system 100 may also include a communication unit connected to the processor 120 .
- the life information processing system 100 can establish data communication with a third-party device through a communication unit.
- the processor 120 also controls the communication unit to obtain data from the third-party device, or sends the vital sign data collected by the data collection unit to the third-party device.
- Communication units include but are not limited to WiFI, Bluetooth, NFC, ZigBee, ultra-wideband UWB or 2G, 3G, 4G, 5G and other mobile communication units.
- the life information processing system 100 can also establish a connection with a third-party device through a cable.
- Third-party equipment includes but is not limited to ventilator equipment, anesthesia machine equipment, infusion pump equipment, image acquisition equipment and other medical equipment.
- Third-party devices can also be cloud service systems or non-medical devices such as mobile phones, tablets, and personal computers.
- the vital information processing system 100 in the embodiment of the present application performs status monitoring. Compared with parameter alarms, status monitoring is clinically It is more complicated. Vital sign data alone is difficult to support the determination of patient status. Therefore, the vital information processing system 100 in the embodiment of the present application acquires multiple types of patient data through multiple channels such as data acquisition units and communication units, and processes multiple types of patient data. Comprehensive analysis of various types of patient data makes the patient status obtained by analysis more accurate, avoiding incorrect patient status or failure to obtain patient status due to insufficient information.
- the life information processing system 100 also includes an alarm unit connected to the processor 120 for outputting alarm prompts so that medical staff can perform corresponding rescue measures.
- Alarm units include but are not limited to alarm lights, alarm speakers, etc.
- the alarm information can also be displayed on the display 130, flash the alarm light to alert medical staff, or play the alarm information through an alarm speaker, etc.
- the life information processing system 100 may also include other input/output devices connected to the processor 120, including but not limited to keyboard, mouse, touch display screen, remote control, etc. Input devices, and output devices including but not limited to printers, speakers, etc.
- FIG. 1 is only an example of components included in the life information processing system 100 and does not constitute a limitation on the life information processing system 100 , and the monitoring device 100 may include more or fewer components than shown in FIG. 1 , or combine certain parts, or different parts.
- the processor 120 obtains multiple types of patient data of the patient to fully determine the patient's status.
- the patient's state may be at least one of the patient's overall state, the state of the physiological system, the state of the organ, the state of the physiological part, and the state of the tissue. Since the patient's nervous system, respiratory system and circulatory system are of greatest clinical concern, the state of the physiological system can include the state of the nervous system, the state of the respiratory system and the state of the circulatory system, as reflected by the state of systems, organs or tissues, etc. are corresponding to the macroscopic state of systems, organs or tissues, etc.
- the patient data includes at least one or more vital sign data of the patient.
- vital sign data includes but is not limited to ECG, blood pressure, pulse oximetry, respiration, body temperature, cardiac output, carbon dioxide, exercise data, video data, respiratory mechanics parameters, hemodynamic parameters, oxygen metabolism parameters, EEG at least one of a parameter, a dual-frequency index and a microcirculation parameter.
- the processor 120 can obtain the vital sign data collected by the vital information processing system 100 itself from the data collection unit, and can also receive the patient's vital sign data from an external device through the communication unit.
- the vital sign data acquired by the processor 120 may be high-precision, high-sampling-frequency vital sign data related to the patient monitored by the vital information processing system 100, such as ECG, blood pressure, respiration, Real-time monitoring data such as blood oxygen, body temperature, and cardiac output, etc., change rapidly with time and are acquired more frequently, and can be presented in real time on the monitoring interface of the life information processing system 100 . Due to different collection conditions, the sampling frequency of vital sign data may be different. For example, blood pressure data is divided into non-invasive blood pressure data and invasive blood pressure data. Invasive blood pressure data is real-time monitoring data, while non-invasive blood pressure data is generally measured intermittently. Data, for example, measure non-invasive blood pressure every 30 minutes to obtain the patient's systolic blood pressure, diastolic blood pressure and mean blood pressure data; similar intermittent measurement data also include data such as body temperature, urine output, blood sugar, etc.
- the processor 120 may obtain vital sign data in multiple ways. For example, one way is to obtain real-time monitored vital sign data, and another way is to obtain vital sign data at one time during patient status monitoring.
- the vital information processing system 100 can also acquire other patient data from external medical equipment or non-medical equipment.
- the external equipment includes treatment equipment, examination equipment and third parties. Systems, etc. Among them, treatment equipment includes ventilators, anesthesia machines, infusion pumps, extracorporeal circulation equipment, etc., inspection equipment includes ultrasound imaging equipment, endoscope equipment, etc., and third-party systems include PACS systems (image archiving and communication systems), LIS system (laboratory information system), CIS system (clinical information system), etc.
- the patient data obtained from the external device includes at least one of the following: monitoring data and/or device data collected by the ventilator device, monitoring data and/or device data collected by the anesthesia machine device, and monitoring data collected by the infusion pump device. and/or equipment data, condition data, inspection data, and inspection data.
- the vital information processing system 100 can obtain the above-mentioned patient data through a communication connection with an external device.
- the condition data includes at least one of basic patient information, disease diagnosis data, treatment data, nursing data and electronic medical record data.
- the patient's basic information includes age, weight, gender, etc.
- the disease diagnosis data includes medical history, diagnosis reports, doctor's orders, consultation conversations, etc.
- the vital information processing system 100 can obtain the patient's condition data through electronic medical records, or receive condition data input by medical staff.
- Test data includes blood routine test data, liver function test data, renal function test data, thyroid test data, urine test data, immune test data, coagulation test data, blood gas test data, stool routine test data and tumor data collected by in vitro diagnostic equipment.
- the vital information processing system 100 can obtain the patient's test data through the hospital laboratory information management system.
- the examination data includes data collected by medical imaging equipment, specifically including at least one of DR image data, CT image data, MRI image data, PET image data, ultrasound image data, scale data, and physical test data.
- the life information processing system 100 Examination data can be obtained from medical imaging equipment or image induction and communication systems.
- the patient data obtained by the processor 120 may be medical data within a certain time range.
- the time range may be a preset time range, such as 24 hours, or an appropriate time range may be set according to user instructions. Specifically, the time range may be set according to the medical data.
- the timestamp selects patient data within a certain time range. If certain types of patient data do not exist within this time range, for example, some laboratory test data may not have test results within 24 hours, you can select the most recently acquired patient data of this type for subsequent data processing.
- the processor 120 can also perform preprocessing, including filtering, exception/null value processing, data sampling alignment and other operations.
- Data sampling alignment refers to the interpolation operation for intermittent measurement data such as blood pressure to ensure that the intermittent measurement data can be analyzed synchronously with other data.
- patient data that meets quality requirements can be extracted for subsequent analysis, especially for continuous measurement data in patient data.
- Quality analysis can help filter out interference-generated data that has no actual clinical value. data.
- the above-mentioned patient data obtained by the vital information processing system 100 can present various aspects of the patient's information, but it can also be seen from the relevant description that there are various patient data related to the patient, and a large part of the data changes over time. It is difficult for medical staff to conduct clinical assessment and decision-making comprehensively and efficiently based on the above patient data. Therefore, the vital information processing system 100 in the embodiment of the present application automatically determines the patient's status based on the patient data, so as to make full use of the patient data and quickly extract effective information from the patient data.
- the processor 120 may determine a target rule that the patient data satisfies, thereby determining the patient's status according to a correspondence between the target rule and the patient's status. Since the data types and amounts of patient data are complex, the patient data can be analyzed first to obtain one or more analysis results that reflect the characteristics of the patient data, and the target rules that the patient data satisfies are determined based on the analysis results.
- the analysis results can be quantitative features or non-quantitative features extracted from the patient data.
- the analysis results may include time information, such as the occurrence moment and duration of the analysis results, etc. Subsequently, based on the time information of the analysis results, the analysis results within the preset time range can be extracted and compared with the preset rules.
- the processor 120 may analyze a single type of patient data to obtain an analysis result, or may analyze at least two types of patient data to obtain an analysis result.
- patient data at the same, similar or adjacent time can be analyzed based on the time information carried in the patient data.
- Analyzing the at least two types of patient data may include calculating a maximum value, a minimum value, an average value of the at least two types of patient data or relevance etc.
- you can choose appropriate correlation calculation methods such as Pearson and Kendall, and set the start and end times of heart rate data and blood pressure data. The start and end times of the two can be exactly the same or have certain differences. .
- the analysis results extracted from the patient data include at least one of the following: parameter values, events, results of secondary processing of parameter values, and results of secondary processing of events.
- the parameter values may include parameter values extracted from the monitoring data collected by the sensor, such as the heart rate value extracted from the heart rate data, the respiration rate value extracted from the respiration rate data, the blood oxygen value extracted from the blood oxygen data, etc.
- Parameter values may also include measurements extracted from imaging data, such as ejection fraction (EF) extracted from ultrasound images.
- Parameter values may also include biochemical index values extracted from biochemical test data, such as arterial partial pressure of oxygen (PaO2), brain natriuretic peptide (BNP), etc.
- Parameter values may also include the patient's height, weight, age, etc.
- Parameter values may also include numerical codes assigned to non-quantified values (such as mental states), for example, using a value of 1 to represent malaise, etc.
- the results of secondary processing of parameter values include operation results obtained using mathematical methods, such as variance, mean, median, maximum value, trend change characteristics, volatility measurement, stationarity description, random characteristics, morphological patterns, Statistical parameters, etc.
- the above-mentioned trend change characteristics are characteristic values that reflect the trend change of parameter values within a period of time. This characteristic value can reflect the direction or speed of change of parameter values. For example, the heart rate increases monotonically at an average rate of +0.3 beats/minute within 1 hour, and the oxygenation index decreases monotonically at an average rate of -0.5 mmHg/minute within 1 hour. The monotonous increase can be represented by the value 1, and the monotonous decrease can be represented by the value 1. -1 means.
- Secondary processing of parameter values may also include further processing of at least two different processing results, such as obtaining new parameters based on the mean and standard deviation of the heart rate.
- Events obtained based on patient data include at least one of the following: over-limit alarms, abnormal events, and clinical events.
- the over-limit alarm is an alarm triggered when the processor 120 detects that the parameter value exceeds the preset alarm limit, including but not limited to heart rate over-limit alarm, blood pressure over-limit alarm, blood oxygen saturation over-limit alarm, etc.; among which "exceeds the limit alarm”
- the "preset alarm limit” can be higher than the upper alarm limit or lower than the lower alarm limit.
- Abnormal events include arrhythmias and other abnormal events that are based on waveform and other characteristics of patient data and are not over-limit alarms.
- Clinical events include examination events, diagnosis events, treatment events, nursing events, etc. recorded in patient data.
- Clinical events can reflect the patient's status. For example, if the patient is inhaling oxygen, it means that the patient may have unstable breathing and poor oxygenation, but the condition is mild and he can still breathe on his own. If a ventilator is used to provide assisted breathing to the patient, the patient's breathing instability will be more serious. The patient may have lost consciousness and cannot breathe on his own.
- the ventilation method used by the ventilator can also reflect the severity of the patient's condition to a certain extent. For example, patients using invasive ventilation may be more severe than those using non-invasive ventilation.
- vasopressors or antihypertensive drugs indicates that the patient may have unstable circulation, so medications are used to maintain stable blood pressure.
- Blood supplementation therapy to maintain adequate blood volume indicates that the patient may have unstable circulation, so blood pressure is maintained stable through blood supplementation.
- Fluid therapy to maintain fluid balance indicates that the patient may have unstable circulation and therefore maintain pressure balance within the body through fluid replacement.
- the results of the secondary processing of events include information such as the occurrence frequency/frequency of the event, the changing trend of the occurrence frequency/frequency, and the duration.
- the frequency of events includes, for example, the number of heart rate exceedances in the past 4 hours, the number of atrial fibrillation occurrences in the past two hours, the number of ventricular tachycardia occurrences in the last 30 minutes, etc.; the changing trend of the frequency includes, for example, the past 4 hours.
- duration Examples include the duration when intracranial pressure (ICP) is too high or too low, the duration when end-tidal carbon dioxide (etCO2) is too high or too low, the duration when mean arterial pressure (MAP) is ⁇ 65mmHg, etc.
- ICP intracranial pressure
- etCO2 end-tidal carbon dioxide
- MAP mean arterial pressure
- the average heart rate in addition to setting the statistical calculation method for calculating the average, you also need to set a time range and extract the heart rate values within the time range for calculating the average.
- the calculated mean is the analysis result corresponding to the heart rate within this time range. That is, for each type of patient data, analysis results at different times can be obtained, and subsequent analysis results at the same or similar time can be processed based on the correspondence between the analysis results and time.
- the vital information processing system 100 can match and judge the analysis results according to the rule base, thereby determining the patient status based on the analysis results.
- the rule base contains a large number of preset rules. There is a pre-established correspondence between the preset rules and the patient's status.
- the preset rules can be formulated based on guideline rules, clinical consensus, clinical research and other methods, and are based on machine learning. Compared with determining patient status by models, determining patient status based on preset rules is more accurate, the determined results are more controllable, and are more in line with the clinical cognition of medical staff.
- the processor 120 determines one or more target rules that the one or more analysis results satisfy based on the one or more analysis results. Among them, each analysis result satisfies a target rule, Alternatively, multiple analysis results satisfy one target rule, or each analysis result satisfies multiple target rules, depending on the preset correspondence between the target rules and the analysis results. The analysis result satisfies a certain target rule, either the analysis result completely satisfies the target rule, or the analysis result is closest to the target rule.
- target rules related to the patient's nervous system include indicators related to cranial nerves
- target rules related to the patient's circulatory system include indicators related to hemodynamics or perfusion
- targets related to the patient's respiratory system include indicators related to the patient's respiratory system.
- the rules include indicators related to oxygenation.
- the processor 120 may select patient data related to cranial nerves, extract analysis results, and determine cranial nerve-related indicators that the analysis results satisfy.
- processor 120 may select patient data related to hemodynamics or perfusion, extract analysis results, and determine that the hemodynamics or perfusion-related metrics are met by the analysis results.
- processor 120 may select patient data related to oxygenation, extract analysis results, and determine oxygenation-related metrics that the analysis results satisfy.
- the processor 120 may compare one or more analysis results with one or more preset rules in the rule base, thereby determining one or more of the analysis results that satisfies one of the one or more preset rules. or multiple target rules.
- One or more preset rules may be stored in the memory 110, and the processor 120 calls the preset rules from the memory 110.
- One or more preset rules may also be stored in the server, and the processor 120 calls the preset rules from the server.
- Each preset rule contains one or more preset conditions for one or more analysis results.
- each preset rule may include a preset condition for a single analysis result, or may include multiple preset conditions for multiple analysis results.
- Preset conditions can include threshold conditions, trend conditions, qualitative conditions, etc.
- Each preset rule defines a corresponding analysis result, and the corresponding relationship between the preset rules and the analysis results can be selected to be included in the analysis results of the preset rules for comparison. For example, for the preset rule "average heart rate greater than 90", select the analysis result of "average heart rate” to compare with it. For example, each preset rule also defines a duration.
- the preset rule the number of times the average heart rate is greater than 90 is greater than 8 times in the past 4 hours
- the analysis result of "The number of times the heart rate is greater than 90” determines whether the number of times the average heart rate is greater than 90 in the past 4 hours is greater than 8 times. If it is greater than 8 times, it means that the preset rule is satisfied, that is, the rule The preset rule is the target rule; otherwise, it means that the preset rule is not satisfied, that is, the preset rule is not the target rule.
- the at least two analysis results may be extracted from the same type of patient data.
- the same preset rule may include preset conditions for heart rate average and Preset conditions for heart rate standard deviation; at least two analysis results can also be extracted from different patient data.
- the same preset rule can include preset conditions for heart rate average and blood oxygen average. Preset conditions.
- At least two analysis results in the same preset rule may be a combination of parameter values (or the results of secondary processing of parameter values) and events (or the results of secondary processing of events).
- at least two analysis results in the same preset rule can be a combination of parameter values and clinical events, thereby reflecting changes in the patient's condition after clinical treatment (such as ventilator-assisted breathing, medication, fluid replenishment, blood replenishment, etc.) is provided. It is used to judge the effect of treatment and determine the development trend of the patient's condition.
- preset rule 1 is that after a ventilator-assisted breathing treatment event, SpO2 gradually increases, indicating that the respiratory system condition has improved;
- preset rule 2 is that after using analgesics for pain relief, etCO2 is too low for too long, or RR is too low If the time is too long, or the SpO2 is low for too long, it means that the painkiller may be overdosed and suppress the respiratory system, and the dosage of the drug needs to be reduced.
- the same preset rule may only correspond to one analysis result.
- the patient when a ventilator has been used to provide respiratory support to a patient, the patient can be considered to be a very critical patient, so "use ventilator" can be used as a separate preset rule, and the corresponding patient status is unable to breathe on his own.
- the current vital information processing system may monitor the changing trend of a single patient data, but does not take into account the correlation between the changing trends of multiple patient data. sex.
- the insignificant change trend of a single patient's data may not reflect the patient's status alone, and it cannot attract the attention of medical staff, so that the information contained therein cannot be effectively used. If multiple patient data present a specific change trend at the same time, it can more accurately reflect the patient's status. Therefore, the vital information processing system 100 in the embodiment of the present application comprehensively considers multiple change trends to determine the patient's patient status. .
- multiple change trends are included in at least one preset rule for comprehensive consideration, and the multiple change trends include at least a first change trend of the first information extracted from the patient data and a second change trend of the second information.
- the first information and the second information may be parameter values extracted from patient data, results of secondary processing of parameter values, events extracted from patient data, or results of secondary processing of events; the first information and the second information Can be extracted from the same type or types of patient data, It can also be extracted from different patient data.
- the first change trend and the second change trend can be an upward trend, a downward trend, a fluctuation trend, a mutation trend, etc.; the first change trend and the second change trend can also be a combination of multiple change trends, such as rising first and then falling, and then falling. Decline and then fluctuate, etc.
- the first information is the heart rate value
- the first change trend is the change trend of the heart rate value (hereinafter referred to as the heart rate change trend)
- the second information is the blood oxygen value
- the second change trend is the change trend of the blood oxygen value (hereinafter referred to as the heart rate change trend).
- blood oxygen change trend for example, after extracting the heart rate value from the heart rate data, determine the change trend of the heart rate value within the first time range, for example, determine that the heart rate value shows an upward trend within the first time range; and, After extracting the blood oxygen value from the blood oxygen data, a change trend of the blood oxygen value in the second time range is determined, for example, it is determined that the blood oxygen value shows a downward trend in the second time range.
- the first time range and the second time range may be exactly the same, at least partially the same, or adjacent.
- There are many ways to determine the change trend Taking the heart rate change trend as an example, you can linearly fit the heart rate value in the first time range, and judge the heart rate change trend based on the slope of the fitted straight line; or take The heart rate values in different time windows are averaged, and the heart rate change trend is determined by comparing the average value; the embodiment of the present application does not limit the calculation method of the change trend.
- the heart rate change trend and the blood oxygen change trend can be compared with the preset conditions included in the preset rules to determine whether the combination of the heart rate change trend and the blood oxygen change trend can reflect Some kind of patient state.
- the preset conditions used for comparison with the heart rate change trend and the blood oxygen change trend may be at least two preset conditions included in the same preset rule.
- the preset rule at least defines a preset heart rate change trend and a preset blood oxygen change trend. Comparing the heart rate change trend and the blood oxygen change trend at least includes determining whether the actual heart rate change trend is consistent with the preset heart rate change trend. Consistent, and whether the actual blood oxygen change trend is consistent with the preset blood oxygen change trend.
- the patient's physiological parameters and other data rarely change alone, but mostly change collaboratively. Analyzing and judging multiple change trends at the same time is more in line with physiological laws and helps improve the accuracy of patient status identification.
- the preset heart rate change trend is an upward trend, and the preset blood oxygen change trend is a downward trend. If the actual heart rate change trend is an upward trend, the actual heart rate change trend is an upward trend, and the preset blood oxygen change trend is a downward trend. If the blood oxygen change trend is a downward trend, the preset rule is met; if the actual heart rate change trend and the actual blood oxygen change trend are both an upward trend or both a downward trend, or the heart rate change trend is a downward trend or the blood oxygen change trend If it is an upward trend, the preset rule is not satisfied; other combinations of heart rate change trends and blood oxygen change trends may also represent other patient states.
- the preset rules can also define the heart rate change trend and the blood oxygen change trend as changing trends in the same direction, then the heart rate change trend If the trend of potential and blood oxygen changes is both upward or downward, both meet the preset rules.
- the preset condition also includes that the heart rate change trend and the blood oxygen change trend have a preset time correlation.
- the time correlation may include that the occurrence time of the heart rate change trend and the blood oxygen change trend are at least partially the same, indicating that the heart rate and blood oxygen change synergistically; or, the time correlation may include the occurrence time between the heart rate change trend and the blood oxygen change trend.
- the time difference does not exceed the preset time difference.
- the blood oxygen change trend occurs within 5 minutes after the heart rate change trend, indicating that the changes in heart rate and blood oxygen have a certain correlation; alternatively, the time correlation can also include the heart rate change trend and The blood oxygen trends all occur within the same larger time frame.
- the preset conditions can also define the sequence relationship between the heart rate change trend and the blood oxygen change trend.
- the blood oxygen change trend needs to occur before the heart rate change trend, otherwise even if The occurrence time of the two overlaps, and the preset conditions are not met.
- the preset conditions may not limit the sequence relationship.
- the processor 120 determines one or more patient states of the patient corresponding to the target rules, which are the patient states that the vital information processing system 100 is targeting for current monitoring.
- the processor 120 may use the patient status corresponding to the target rule as the determination result of the patient status according to the preset correspondence relationship between the target rule and the patient status.
- the patient status determined by the processor 120 is usually an abnormal status because the abnormal status requires more attention from medical staff; however, the patient status may also be a normal status.
- the processor 120 can determine that the patient's state is a circulatory unstable state according to the target rules. When the circulatory unstable state is not determined, the circulatory system will be defaulted to a circulatory stable state; the processor 120 can also determine according to some target rules.
- the patient's status is circulatory stability.
- the patient status in the embodiment of the present application may be the prediction result of the patient's future status or the judgment result of the patient's current status.
- the patient status such as "circulatory instability” in the following can refer to the patient's current circulatory instability, or it can also refer to the patient's possibility of circulatory instability in the future.
- the prediction results of the patient's future status represent the development trend of the patient's status. Presenting the prediction results of the patient's future status can help medical staff intervene early to avoid or slow down the deterioration of the patient's status. Presenting the judgment results of the patient's current status also helps medical staff to provide targeted treatment in a timely manner.
- the vital information processing system 100 of the embodiment of the present application can determine the patient status based on patient data. Based on the vital information processing system 100, a variety of patient data are summarized, eliminating the need for medical staff to conduct subjective analysis of large amounts of data, and directly providing medical staff with the clearest and most concise information that requires the most attention. When the prediction results of the patient's future status are output, treatment can be quickly stopped when the patient's status shows signs of deterioration, and the deterioration of the condition can be prevented as early as possible.
- the processor 120 may determine one or more patient states corresponding to one or more target rules based on the correspondence between the preset rules and the preset patient states.
- the corresponding relationship between the preset rules and the preset patient status can be stored in the memory 110.
- the processor 120 determines the target rule matching the analysis result in the preset rules
- the processor 120 determines the corresponding relationship between the preset rules and the patient status stored in the memory 110. The corresponding relationship between them determines one or more patient states corresponding to the target rule.
- each target rule corresponds to a patient status, that is, if one target rule is satisfied, the patient is determined to have a patient status corresponding to the target rule; or, each target rule corresponds to multiple patient statuses, that is, as long as one target rule is satisfied rules, it is determined that the patient has multiple patient states at the same time; or, multiple target rules correspond to one patient state, that is, only when multiple target rules are met at the same time, the patient can be determined to have the patient status corresponding to the multiple target rules.
- the patient's status may include the status of multiple physiological structures of the patient.
- the physiological structures at least include the patient's physiological system, and the physiology includes the circulatory system, respiratory system and nervous system that clinical medical staff are most concerned about.
- physiological systems can also include the motor system, endocrine system, digestive system, urinary system, reproductive system, etc.
- the state of the physiological system is a direct reflection of the overall situation of the physiological system. Since the physiological system of the human body is composed of multiple tissues or organs, previous monitoring equipment cannot evaluate the overall situation of the physiological system.
- the life information processing system 100 in the embodiment of the present application integrates multiple types and multiple sources of patient data, based on rules. The library enables a comprehensive and summary assessment of physiological system status.
- the target rule includes the patient's cranial nerve-related indicators.
- the target rule includes the patient's hemodynamic-related or perfusion-related indicators.
- the target rule includes indicators related to the patient's oxygenation.
- the patient's status can also include the patient's overall status, the status of physiological systems, the status of organs, the status of physiological parts, the status of tissues, etc.
- the organ includes at least one of a brain, a heart, a lung, a liver, a stomach, and a kidney.
- Physiological parts include head, chest, abdomen, etc.; tissues include muscle tissue, nervous tissue, epithelial tissue, etc.
- Patient status can include the patient as a whole, physiological systems, organs, physiological parts, and tissues.
- One or more clinically defined macroscopic states reflect the overall physiological function of the part, such as circulatory instability, insufficient perfusion, etc. of the circulatory system.
- the patient states corresponding to different physiological structures can be regarded as responses to the physiological structure. Section summary.
- the patient's status may specifically include the deterioration of physiological structures such as systems and organs, for example, whether the physiological structure is currently deteriorating or whether it is likely to deteriorate in the future.
- the patient's status can also include the abnormality of systems, organs and other physiological structures, abnormality level, criticality level, care level, etc.
- the status of the circulatory system may include abnormal circulation or normal circulation, and the abnormality of circulation may further include mild abnormality of circulation or severe abnormality of circulation.
- patient status may also include specific diseases related to the patient as a whole, the patient's physiological system, organ, physiological part or tissue, such as acute respiratory distress syndrome (ARDS), respiratory failure, acute kidney injury (AKI), Sepsis, heart failure, brain damage, etc.
- ARDS acute respiratory distress syndrome
- AKI acute kidney injury
- Sepsis Sepsis
- heart failure heart failure
- brain damage etc.
- the patient status may also include an unknown status or a suspected abnormal status.
- the patient status can be regarded as a comprehensive evaluation
- the vital information processing system 100 can separately determine the status of different physiological structures of the patient.
- the vital information processing system 100 can configure corresponding patient data types, data analysis methods and preset rules for each physiological structure. After obtaining multiple types of patient data of the patient, the patient data can be classified according to the corresponding relationship between the physiological structure and the patient data, and then the patient data can be analyzed and analyzed according to the corresponding relationship between the physiological structure and the data analysis methods and preset rules. judge.
- patient data related to the respiratory system can be extracted from multiple types of patient data, relevant analysis results can be obtained based on the preconfigured data analysis method, and relevant analysis results can be obtained based on the respiratory system-related data.
- the target rule that these analysis results satisfy is determined among multiple preset rules, thereby obtaining the target rule related to the respiratory system; finally, the status of the respiratory system is obtained based on the corresponding relationship between the target rule and the patient's status.
- the vital information processing system 100 can be pre-configured with multiple physiological structures, and the user can select a target physiological structure among them as needed, so as to view the patient status of a specific physiological structure in a targeted manner.
- the vital information processing system 100 can also configure different section combination templates for different types of patients or different types of users, and automatically select target clinical sections from multiple physiological structures according to the current patient type or user type, thereby intelligently presenting Different types of patients or patient status that are of concern to different types of users.
- the vital information processing system 100 may only determine the patient status corresponding to the target physiological structure, or the vital information processing system 100 may determine the patient status corresponding to multiple physiological structures, but only present the patient status corresponding to the target physiological structure.
- the patient data type corresponding to each physiological structure can also be customized by the user, and the patient data type corresponding to each physiological structure can be determined according to the received user instructions.
- Users can select the target physiological structure and the patient data type corresponding to the target physiological structure by considering the patient's illness (such as heart failure, injury, respiratory failure, etc.), hospital equipment, hospital conditions and other factors.
- the section combination template can not only be used by users who customize the template, but users can also share or publish their customized templates for other people to use. In practical applications, users can easily select the currently used section combination template from multiple section combination templates.
- the multiple section combination templates can be created by the current user or by other users.
- the processor 120 may also determine the patient status of the patient based on the first machine learning model for determining the patient status.
- the first machine learning model can be used as an auxiliary tool for preset rules to improve the accuracy of patient status.
- the processor 120 may input the patient data or the analysis results extracted from the patient data or the target rules satisfied by the analysis results into the first machine learning model, obtain the second patient status output by the first machine learning model, and The final determination result of the patient status is obtained based on the second patient status output by the first machine learning model and the first patient status determined based on the target rule.
- the final determination result of the patient's status may include the union of the first patient status and the second patient status, that is, as long as a certain patient status is determined based on any one of the rule base and the first machine learning model, the patient is considered to be present.
- the final determination result of the patient status can also include the intersection of the first patient status and the second patient status, that is, the patient is considered to have appeared only when a certain patient status is determined based on both the rule base and the first machine learning model.
- the patient status; or the first patient status and the second patient status can also be fused based on other strategies to obtain a more accurate patient status.
- the processor 120 may also extract analysis results from the patient data based on the first machine learning model, or perform secondary processing on the analysis results extracted from the patient data based on the first machine learning model; the processor 120 may also extract analysis results based on the first machine learning model.
- a machine learning model performs secondary processing on the patient status obtained based on the target rules.
- the embodiment of the present application determines the patient's status according to the preset rules.
- the machine learning model can be used as an assistant to improve the accuracy of the patient's status, and the reasons for the patient's status can be explained by the target rules.
- the processor 120 may also independently obtain the patient status using a machine learning model.
- the processor 120 can input multiple different types of patient data into the machine learning model to obtain the patient status output by the machine learning model, or the processor 120 can input analysis results extracted from the patient data into the machine learning model. , obtain the patient status output by the machine learning model; that is, the input of the machine learning model can be the original patient data or the analysis results extracted from the patient data.
- the accuracy of machine learning models in identifying patient status is also constantly improving.
- the embodiment of the present application determines the target rule based on the analysis results extracted from the patient data, so that the reason why the machine learning model obtains the patient's status can be explained through the target rules.
- the processor 120 controls the display 130 to simultaneously display the patient status and the target rule
- the patient status may be determined based on the target rule, may be determined based on the machine learning model, or may be determined in combination with the target rule and the machine learning model. Even if the patient state is not determined based on the goal rule, the goal rule is related to the patient state to some extent.
- the target rule corresponding to the patient status can be deduced from the patient status, and the target rule can be displayed.
- the processor 120 controls the display 130 to display patient status prompt information representing the patient's status; further, the processor 120 can also control the display 130 to display an explanatory explanation of the obtained patient status, This provides support for the patient's status, increases the credibility of the patient's status, and better assists users in diagnosing and treating patients.
- the explanatory description of the patient's status may include at least a part of one or more target rules.
- Presenting patient status prompt information on the display 130 can remind medical staff to pay attention to the patient in a timely manner, especially to take timely response measures when the patient's status is abnormal; displaying target rules related to the patient status can inform medical staff why the vital information processing system 100 obtains the current
- the displayed patient status increases the credibility of the patient's status. On the other hand, it also helps medical staff provide timely symptomatic treatment.
- the processor 120 can update the patient status displayed on the display 130 according to a preset update cycle.
- the preset update cycle can be preset by the vital information processing system 100 or input or changed by the user, which can be measured in minutes. , in hours, etc.
- the processor 120 may also continuously monitor in the background The patient's status is monitored, and when a change in the patient's status is detected, the display 130 is controlled to update the displayed patient status.
- the patient status prompt information includes an overall assessment of the status of at least one physiological structure of the patient's physiological structure, which briefly summarizes the overall status of the entire physiological structure and provides summary information about the entire physiological structure.
- Physiological structures include the patient's physiological systems, physiological organs, physiological parts, tissues, characteristics of physiological systems or characteristics of physiological organs.
- Physiological systems include the motor system, nervous system, endocrine system, circulatory system, respiratory system, digestive system, urinary system and At least one of the reproductive system; physiological organs include at least one of the brain, heart, lungs, liver, stomach, and kidneys; physiological parts include at least one of the head, chest, and abdomen; tissues include muscle tissue, nervous tissue, and epithelial tissue At least one of; the characteristics of the physiological system or the characteristics of the physiological organ include at least one of input and output, coagulation, nutrition, infection, blood sugar and medical events.
- the processor 120 can control the display 130 to display patient status prompt information through text or graphics.
- text and graphics can be combined.
- strings related to the patient's status can be pre-configured.
- the strings include strings characterizing the patient as a whole, physiological system, organ, physiological part or tissue, as well as strings characterizing specific states, such as circulatory system + possible Shock/possible heart failure/possible internal bleeding, respiratory system + possible respiratory depression, nervous system + possible cerebral hemorrhage, urinary system + possible kidney failure, immune system + possible serious infection, etc.
- the string can have various forms, as long as it can reflect the patient's status.
- the string used to characterize heart failure can be "the circulatory system may have heart failure", “the patient may have heart failure”, “the patient is at risk of heart failure”, etc. .
- Strings can be preset by experts for each patient status, or can be adjusted to the current specific patient status using methods related to natural language processing. For example, the string may also allow the user to configure or modify it.
- graphics related to each patient's status that can vividly represent the patient's status can be stored in advance, and the graphics can be called for display after the patient's status is determined.
- the graphics representing the patient's status may correspond to the patient as a whole, physiological system, organ, physiological part or tissue, and the graphics are modified by at least one of symbol information, color information and text information located on or near the graphics.
- Markers to present patient status For example, displaying a graph representing the circulatory system in red indicates that the circulatory system status is abnormal, and in green indicating that the circulatory system status is normal.
- Graphics used to represent the patient's status can be displayed in the display area corresponding to each patient's status, so that medical staff can view them respectively.
- graphics used to represent the circulatory system status are displayed in the display area corresponding to the circulatory system.
- Graphics representing the status of the respiratory system are displayed in the corresponding The display area, graphics used to represent the state of the nervous system are displayed in the display area corresponding to the nervous system, etc.
- graphics used to represent the patient's status can also be displayed in the same human body status indication diagram to facilitate medical staff to have an overview of the patient's status.
- the human body status indicator diagram can be a full-body diagram, a half-body diagram, etc., which can display multiple human organs or systems. For example, a graphic representing the state of the nervous system is displayed at the head position of the human body status indicator diagram, a graphic representing the status of the circulatory system is displayed at the heart position of the human body status indicator diagram, and a graphic is displayed at the chest position of the human body status indicator diagram. Graphics showing the status of the respiratory system, graphics showing the status of the digestive system, etc. are displayed on the abdomen of the human body status indicator diagram.
- the human body status indicator diagram can completely display multiple organs or systems, or it can only display the organs or systems corresponding to the target physiological structure, the organs or systems currently experiencing abnormal conditions, or the organs or systems selected by the user, etc.
- the graphics used to represent the status of each system or organ can be a graphic drawing of the corresponding system or organ, and the color or dynamic change of the graphics can be used to present the status of the corresponding system or organ.
- the overall state of the patient can also be represented by a human body graphic. For example, the overall state of sepsis or systemic infection can be represented by the boundary lines of the human body graphic.
- the processor 120 can control the display 130 to differentiate between the prediction results of the patient's future status and the judgment results of the patient's current status, so that the user can determine which patient status is the current patient status and which patient status is likely to occur in the future. patient status.
- At least part of one or more target rules may be displayed adjacent to the corresponding patient status to reflect the correlation between the target rule and the patient status.
- Displaying at least a part of one or more target rules may include displaying several target rules with a higher degree of importance among the multiple target rules. Since there may be a large number of target rules related to the patient's status, select the ones with a higher degree of importance. Displaying target rules helps medical staff focus on them, without the need for medical staff to manually filter among multiple rules.
- the displayed target rules can also be sorted according to the degree of importance, so that medical staff can give priority to target rules with higher importance. In some embodiments, the importance of the target rule can also be marked by color, graphics, text, etc.
- Displaying at least part of one or more target rules may also include displaying several preset conditions with higher importance among the multiple target rules.
- each target rule can include one or more preset conditions, and the final displayed preset conditions can be part of the conditions in each target rule, that is, medical staff can be selected to pay more attention to in each rule. display the preset condition without displaying other preset conditions in the rule.
- some targeting rules include targeting patients Preset conditions such as age, weight, condition, etc. These preset conditions are related to the threshold of preset conditions for vital sign data. Although it has an important impact on the judgment of the patient's status, medical staff usually do not pay attention to this type of information.
- the processor 120 may also determine a state level that represents the severity of the patient's state, and control the display 130 to display state level prompt information that represents the state level.
- the processor 120 can combine multiple patient states to jointly determine a comprehensive status level, for example, the patient's overall status represents a level that represents serious deterioration; the comprehensive status level can be used to prompt medical staff which patients need the most attention.
- the processor 120 can separately determine a separate status level for each patient's status, such as separately determining status levels for the status of the respiratory system, nervous system, and circulatory system; the individual status levels can be used to prompt medical staff which aspects of the current patient are most important. Need attention.
- the embodiment of the present application determines the physiological cause causing the patient's state, and controls the display 130 to display the physiological cause prompt information characterizing the physiological cause, thereby providing the user with a deeper explanatory explanation about the patient's state.
- the physiological cause is the physiological cause of the current patient's state determined based on biomedical principles, clinical experience, etc., and may specifically be a certain disease or injury (i.e., the cause).
- a physiological cause can also be a patient state on the other hand.
- a physiological cause related to a patient state of a physiological system may be the state of a certain tissue or organ in that physiological system.
- a deterioration of the state of a certain tissue or organ causes the entire system to deterioration; alternatively, the physiological cause may be the state of another physiological system, for example, the deterioration of the state of one physiological system leads to the deterioration of the state of another physiological system.
- the processor 120 may determine the physiological cause causing the patient's state according to the correspondence between the preset rules and the physiological cause.
- the memory 110 may store the correspondence between the preset rules and the preset physiological causes. After determining that at least one target rule is satisfied by the analysis results extracted from the patient data, based on the correspondence, determine the cause of the patient corresponding to the at least one target rule.
- Physiological causes of the condition Each preset rule may correspond to one physiological cause, or each preset rule may correspond to multiple physiological causes, or multiple preset rules may correspond to one physiological cause.
- the processor 120 may also determine the physiological cause based on a second machine learning
- the model is used to determine the physiological cause of the patient's state. Specifically, the patient data, one or more analysis results extracted from the patient data, or the target rules satisfied by the analysis results can be input into the second machine learning model, and the third machine learning model can be obtained. 2. Physiological reasons for machine learning model output.
- the processor 120 may control the display 130 to display at least part of the target rule in association with the physiological cause prompt information.
- the associated display may include presenting the correlation between the physiological cause and the target rule through specific characters, or the associated display may include displaying the associated target rule and the physiological cause adjacently or in the same area.
- the processor 120 determines that the analysis results obtained from the patient data meet the target rules including the following: Target rule 1. MAP is between 52-94 mmhg, and 22% Time less than 65mmhg; Goal rule 2.Sl>0.7, and MSl>0.9, and ASl>47; Goal rule 3.HR rises, and SpO2 falls.
- the processor 120 determines that the state of the patient's circulatory system is circulatory instability. Based on the correspondence between the above target rules and physiological causes, the processor 120 determines that the physiological cause corresponding to target rule 1 is the risk of long-term hypoperfusion; the physiological cause corresponding to target rule 2 is possible hypoperfusion; the physiological cause corresponding to target rule 3 is The reason is insufficient oxygenation. Based on the above information, the processor 120 controls the display 130 to display physiological cause prompt information indicating circulatory instability; displays target rule 1, target rule 2 and target rule 3; and displays the physiological cause corresponding to the target rule after each target rule. .
- the processor 120 controls the display to display the target rules to prompt the reasons for obtaining the patient status.
- the processor 120 can also control the display 130 to display the rules extracted from the patient data used to obtain the patient status. At least part of the information to remind medical staff the basis for determining the patient's status.
- key information extracted from the patient data used by the machine learning model to obtain the patient status may be determined and displayed.
- the first machine learning model may output the patient At the same time, it outputs the key information that contributes the most to the patient's status, showing that this key information can explain the reason why the first machine learning model obtains the patient's status, and increases the credibility of the patient's status.
- the machine learning model can also output the weight of the contribution of each key information, and the display 130 can display each key information in order from high to low weight.
- the information extracted from the patient data may include the above-mentioned parameter values extracted from the patient data, the results of secondary processing of the parameter values, events, and the results of secondary processing of the events. results; in addition, the information extracted from the patient data can also be used to obtain a certain waveform or trend chart based on the patient's status, and its presentation form can be to directly present the waveform chart, trend chart, and other statistics. diagrams, etc., without having to present the information extracted from the patient data in the form of characters.
- the waveform graph reflects the changes in patient data over time in one or more cycles.
- the potential changes on the body surface are recorded to form a continuous curve, which is the electrocardiogram waveform graph;
- a corresponding set of waveforms can appear on the electrocardiogram waveform for each cardiac cycle.
- Similar ones include blood oxygen saturation waveform, end-tidal carbon dioxide waveform, etc.
- Trend charts are used to reflect the development trend of one or more patient data over time. Compared with waveform charts, the time dimension is longer.
- the vertical axis of the trend chart can be either the absolute value collected at a certain sampling rate, or the average value within each fixed time period collected and calculated at a certain sampling rate.
- the trend graph can be one of curve graphs, histograms, bar graphs, box plots, scatter plots, and line graphs, or it can be a curve graph, histogram, bar graph, box plot, scatter plot, or line graph. various combinations.
- the trend chart can be used to reflect the trend of events identified from the patient data over time, for example, the occurrence form, development trend, incidence rate, trend, etc. of event changes and development over time.
- occurrence rate can be the frequency of occurrence of an event within the smallest unit of time
- the occurrence form, development trend, trend, etc. are not limited to the frequency of occurrence, but can be marked based on the actual occurrence of the event, such as , take the atrial fibrillation event as an example.
- graphics/symbols When the atrial fibrillation event occurs continuously and for a long time, graphics/symbols, etc. can be used to follow the horizontal stroke.
- the length of the axis spanned reflects the duration of the atrial fibrillation event.
- the same trend chart can also be used to present changes in at least two types of patient data over time.
- the trend chart of heart rate, pulse rate, blood oxygen, non-invasive blood pressure, and invasive blood pressure can be combined.
- Trend graph, respiratory trend graph, body temperature trend graph, stroke cardiac output trend graph, cardiac output trend graph, electrocardiogram ST segment, electrocardiogram QT interval, blood sugar, brain oxygen, and urine output, at least two of them Display simultaneously, that is, share the same timeline, or align the timelines for display. Displaying trend charts of at least two patient data at the same time helps to jointly present the correlation of changes in different patient data during the monitoring period.
- the processor 120 can also perform noise assessment on the patient data to confirm the data quality of the patient data.
- the noise assessment can help determine whether the currently obtained patient status is reliable, for example, the data quality of certain patient data When it is too poor, the patient status will not be determined based on the patient data to prevent the output of untrustworthy information from interfering with the doctor's normal clinical diagnosis and treatment; alternatively, the data quality parameters can be displayed while displaying the patient status, and the data quality parameter reflection is used to obtain the patient information. status of patient data to assist medical staff in confirming the current The degree of credibility of the previously displayed patient status.
- the above-mentioned patient status, target rules, physiological reasons and other information in the embodiment of the present application can be displayed in the patient status window 610.
- the patient status window 610 can be a window superimposed on the regular monitoring interface, and can be displayed by the user. Choose whether to display the patient status window, or automatically pop up the patient status window when the patient status is abnormal; alternatively, the above patient status, target rules and physiological reasons and other information can be displayed in the patient status area of the regular monitoring interface, that is, in the regular monitoring interface Allocate a fixed display area for information such as patient status, target rules, and physiological reasons.
- the conventional monitoring interface displays real-time monitoring values of conventional vital signs data, waveforms, parameter alarm information, etc.
- the above information such as patient status, target rules and physiological reasons can also be displayed in the patient status monitoring interface.
- the patient status monitoring interface displays information on multiple physiological structures of the patient, at least one physiological structure.
- the information includes summary information 510 corresponding to the physiological structure.
- the user can switch between the regular monitoring interface and the patient status monitoring interface. For example, by clicking on the controls on the regular monitoring interface in Figure 6, the patient status monitoring interface in Figure 5 can be entered; or the patient status can also be displayed directly on the monitor. Monitoring interface.
- the above information is also provided to the doctor by outputting an electronic report or printing a paper report through a printing device.
- the information of different physiological structures can be displayed in different display areas.
- each physiological structure corresponds to an independent card, or the information of different physiological structures can also be displayed in different display areas. Use color to distinguish, or use border lines to distinguish, etc.
- the display area corresponding to at least one physiological structure displays summary information about the physiological structure.
- the information extracted from the patient data displayed in the display area corresponding to the respiratory system includes at least one of the following: oxygenation index (PaO2/FiO2), blood oxygen saturation (SpO2), respiratory rate (RR), inhalation Oxygen concentration (FiO2), end-tidal carbon dioxide, blood gas analysis parameters, and parameters of ventilators or oxygen therapy equipment, etc.
- the measured values of blood gas analysis parameters include lactic acid (Lac), arterial oxygen partial pressure (PaO2), arterial carbon dioxide partial pressure (PaCO2), etc.;
- the measured values of ventilator parameters include tidal volume (Tv), positive end-expiratory pressure (PEEP), as well as the current mode of oxygen delivery to the patient, such as SIMV ventilation mode, intubation or mask, etc.
- the display interface can display the measured value of the parameter and/or the change trend graph of the parameter. For the measured value that exceeds the normal range, a mark can be given to highlight it. By way of example, changes between these parameters and the last measured parameters can also be provided. For parameters that only display parameter measurement values, the value and change trend diagram within a preset time period can also be displayed in response to a selection instruction for the parameter measurement value.
- Summary information 510 of the respiratory system is also displayed in the display area corresponding to the respiratory system.
- the summary information of the system can include a general summary of the overall status of the respiratory system (such as unstable breathing), current problems of the respiratory system (such as transient hypoxemia, intermittent reduced compliance), and suggestions provided to the user. (For example, please consider adjusting the ventilatory support mode or parameters), possible risks (risk of pressure injury), etc.
- the information extracted from respiratory system-related data displayed on the display interface may be the same or different from the information used to obtain the status of the respiratory system.
- the target rules for obtaining the status of the respiratory system mainly include indicators related to the oxygenation of the patient.
- an entry for manual clinical assessment tools related to the respiratory system is also displayed in the display area corresponding to the respiratory system, and the user can select this entry to activate the manual clinical assessment tool.
- the information displayed in the display area corresponding to the circulatory system mainly includes information extracted from hemodynamics and perfusion-related data.
- Patient data related to the circulatory system include shock index, blood pressure, cardiac output, lactate (Lac), as well as laboratory indicators and hemodynamic parameters related to hemodynamics and perfusion.
- blood pressure can be invasive blood pressure or non-invasive blood pressure.
- Laboratory indicators include but are not limited to hemoglobin (Hb or HGB), red blood cell count (RBC), pH, HCO3, and base excess (BE); hemodynamic parameters include but are not limited to central venous pressure (CVP) and peripheral vascular resistance.
- patient data related to the circulatory system also includes information about support equipment or treatment equipment related to the circulatory system, specifically including the treatment mode used by the support equipment or treatment equipment, key parameters of the equipment, etc. For example, whether to use extracorporeal circulation support equipment such as ECMO; whether to use a balloon reflux pump IABP; whether to use vasoactive drugs, etc.
- the status of the circulatory system is also displayed in the display area corresponding to the circulatory system.
- the information extracted from the circulatory system-related data displayed on the display interface may be the same or different from the information used to obtain the status of the circulatory system.
- Target rules for deriving the status of the circulatory system mainly include hemodynamic-related or perfusion-related indicators.
- the information displayed in the display area corresponding to the nervous system mainly includes information extracted from patient data related to the cranial nerves.
- the patient data related to the nervous system includes consciousness scores, brain blood pressure and blood oxygen indicators, clinical evaluation results related to the nervous system, etc.
- the commonly used clinical consciousness score is the GCS score (Glasgow Coma Score), but users are also allowed to define their own consciousness scoring rules.
- Clinical evaluation results related to the nervous system include evaluation results of pupil size, pupillary light reflex evaluation results, limb muscle strength evaluation results, etc.
- the state of the nervous system is also displayed in the display area corresponding to the nervous system.
- the information extracted from the data related to the nervous system displayed on the display interface may be the same or different from the information used to obtain the state of the nervous system.
- Target rules for the state of the nervous system mainly include indicators related to the patient's brain nerves.
- the information displayed in the display area corresponding to the heart mainly includes heart-related risk assessment results, such as TIMI (Thrombolysis for Myocardial Infarction) score. If the medical subject undergoes a GRACE (Global Registry of Acute Coronary Syndrome) assessment, the cardiac-related risk assessment results may also include a GRACE score.
- the information displayed in the display area corresponding to the heart also includes heart rate and heart-related biochemical indicators, such as creatine kinase isoenzyme (CK-MB), troponin (cTn), natriuretic titanium (NT-proBNP) ), etc.; and heart-related alarm events, such as ST segment elevation or depression events. For serious arrhythmia events, information on fatal arrhythmia events can be displayed, including the number of arrhythmia occurrences in the past period.
- the information displayed in the display area corresponding to the liver mainly includes liver function evaluation indicators, such as alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), total bilirubin (Tbil), direct bilirubin (Dbil ), blood ammonia (AMM).
- liver function evaluation indicators such as alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), total bilirubin (Tbil), direct bilirubin (Dbil ), blood ammonia (AMM).
- ALT alanine aminotransferase
- GTT gamma-glutamyl transferase
- Tbil total bilirubin
- Dbil direct bilirubin
- AAM blood ammonia
- the change information between the latest indicator and the previous indicator is displayed.
- Users can customize the liver function assessment indicators they wish to view. For example, if the medical subject undergoes a liver function assessment
- the information displayed in the display area corresponding to the kidneys mainly includes urine volume and fluid input and output.
- the liquid input and output includes the liquid input and output, where the liquid input includes the total input in 24 hours, and the amount of liquid pumped into the human body by the infusion pump in 24 hours. Furthermore, the amount of liquid input may also include the amount of dietary liquid, etc.
- Fluid output includes 24-hour urine output, 24-hour drainage fluid volume, and other equipment dehydration fluid volumes, etc. Fluid output can also include sweating, excretion, vomiting, bleeding fluid volume, etc.
- a human body status indication diagram is also displayed in the patient status monitoring interface, and the human body status indication diagram can provide an overview of the patient's status.
- the human body status indication diagram can be displayed in the center of the patient status monitoring interface, but is not limited to this.
- the human body status indicator diagram can completely display multiple organs or systems, or it can only display the organs or systems corresponding to the target physiological structure, the organs or systems currently experiencing abnormal conditions, or the organs or systems selected by the user, etc.
- the graphics used to represent the status of each system or organ can be a graphic drawing of the corresponding system or organ, and the color or dynamic change of the graphics can be used to present the status of the corresponding system or organ.
- the overall state of the patient can also be represented by a human body graphic. For example, the overall state of sepsis or systemic infection can be represented by the boundary lines of the human body graphic.
- the vital information processing system 100 in the embodiment of the present application comprehensively analyzes multiple types of patient data to obtain patient status, fully considers the correlation between multiple patient data, and can avoid false alarms and missed alarms. and other problems, and can help medical staff quickly and reliably judge the patient's condition, which is conducive to real-time control of the patient's health status; in the process of determining the patient's status Taking into account the correlation of multiple changing trends of patient data, it can improve the accuracy of patient status judgment.
- Figure 3 is a schematic flow chart of the life information processing method 300 according to the embodiment of the present application.
- the life information processing method 300 in the embodiment of the present application includes the following steps:
- step S310 multiple types of patient data of the patient are obtained.
- step S320 the patient data is analyzed to obtain multiple analysis results, wherein the multiple analysis results at least include a first change trend of the first information extracted from the patient data and a second change trend of the second information;
- step S330 determine whether the first change trend and the second change trend meet the preset conditions
- step S340 when it is determined that the first change trend and the second change trend satisfy the preset condition, determine the patient status of the patient related to the preset condition.
- step S350 patient status prompt information representing the patient's status is displayed, and the first change trend and the second change trend are displayed.
- the preset conditions include: the first change trend is an upward trend, a downward trend or a fluctuation trend; the second change trend is an upward trend, a downward trend or a fluctuation trend; the first change trend and the second change trend have preset Temporal correlation. Further, the time correlation can be displayed.
- the patient's state includes at least one of the patient's overall state, the state of the physiological system, the state of the organ, the state of the physiological part, and the state of the tissue.
- the physiological system includes at least one of the patient's nervous system, circulatory system and respiratory system; when the physiological system includes the patient's nervous system, at least one of the first change trend and the second change trend includes changes related to the patient's cranial nerves.
- the indicator when the physiological system includes the patient's circulatory system, at least one of the first change trend and the second change trend includes a hemodynamic-related or perfusion-related indicator of the patient, when the physiological system includes the patient's respiratory system , at least one of the first change trend and the second change trend includes an oxygenation-related indicator of the patient.
- this embodiment of the present application provides a life information processing method 400, which includes the following steps:
- step S410 multiple types of patient data of the patient are obtained, where the patient data at least includes one or more vital sign data of the patient;
- step S420 the patient data is analyzed to obtain multiple analysis results, wherein the multiple analysis results at least include a first change trend of the first information extracted from the patient data and a first change trend of the second information.
- second change trend the first information and the second information are related to the patient's Related to the same patient state, the patient state at least includes the state of the patient's physiological system;
- step S430 determine whether the first change trend and the second change trend meet preset conditions
- step S440 when it is determined that the first change trend and the second change trend satisfy the preset condition, the first change trend and the second change trend are displayed.
- the preset conditions include: the first change trend is an upward trend, a downward trend, or a fluctuation trend; and the second change trend is an upward trend, a downward trend, or a fluctuation trend. Further, the preset condition also includes that the first change trend and the second change trend have a preset time correlation, and the time correlation can be displayed.
- the patient's physiological system includes at least one of the patient's nervous system, circulatory system, and respiratory system; when the physiological system includes the patient's nervous system, at least one of the first change trend and the second change trend includes a relationship with the patient.
- Cranial nerve-related indicators when the physiological system includes the patient's circulatory system, at least one of the first change trend and the second change trend includes a hemodynamic-related or perfusion-related indicator of the patient, when the physiological system includes the patient
- at least one of the first change trend and the second change trend includes an oxygenation-related indicator of the patient.
- the patient's state also includes at least one of the patient's overall state, the state of organs, the state of physiological parts, and the state of tissues.
- the life information processing method 300 and the life information processing method 400 of this embodiment can be implemented in the life information processing system 100 described above. Specifically, the life information processing method 300 can be executed by the processor 120 of the life information processing system 100 Or each step of the life information processing method 400. For specific details of the life information processing method, please refer to the relevant description of the life information processing system 100 above, and will not be described again here.
- the disclosed devices and methods This can be achieved in other ways.
- the device embodiments described above are only illustrative.
- the division of the units is only a logical function division. In actual implementation, there may be other division methods.
- multiple units or components may be combined or can be integrated into another device, or some features can be ignored, or not implemented.
- Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof.
- a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some modules according to embodiments of the present application.
- DSP digital signal processor
- the present application may also be implemented as a device program (eg, computer program and computer program product) for performing part or all of the methods described herein.
- Such a program implementing the present application may be stored on a computer-readable medium, or may be in the form of one or more signals. Such signals can be downloaded from Internet sites obtained, or provided on a carrier signal, or in any other form.
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Biomedical Technology (AREA)
- Public Health (AREA)
- Pathology (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
一种生命信息处理系统和生命信息处理方法，该系统包括存储器、处理器和显示器，处理器执行以下操作：获取病人的多个类型的病人数据；对病人数据进行分析，以得到多个分析结果，其中，多个分析结果至少包括从病人数据中提取的第一信息的第一变化趋势和第二信息的第二变化趋势；判断第一变化趋势和第二变化趋势是否满足预设条件；当判断第一变化趋势和第二变化趋势满足预设条件时，确定与预设条件相关的病人的病人状态；显示表征病人状态的病人状态提示信息，以及显示第一变化趋势和第二变化趋势。该系统在确定病人状态时考虑到了病人数据多种变化趋势之间的相关性，能够提高病人状态确定的准确性。
Description
说明书
本申请涉及生命信息技术领域，更具体地涉及一种生命信息处理系统和生命信息处理方法。
随着监测技术发展，目前的监护设备已经能够实时监测人体的心电、血压、血氧饱和度、呼吸频率、体温等重要生命体征参数，并基于这些体征参数的高精度、高采样率信号为医学临床诊断提供重要的病人信息，进行对各参数的监督报警。
传统的监护策略主要聚焦于生理参数的监测，即获取生理信号，基于信号提取生命体征参数，再判断参数是否超过预设数值，如果超出阈值则发出声光、图文报警，提示医护人员当前特定病患某些具体生命体征数值超标。上述分析方法没有有效利用目前已经充分发展的监护技术所提供的大量生命体征参数信息，不能提供直观、方便的病情状态变化指示，只是病人生理参数的客观描述，极大依赖于医生经验对于生命体征参数的解读，资历相对有限的医生容易出现忽略多个参数的同步变化所暗含的病情恶化，耽误了病人的诊断治疗。
发明内容
在发明内容部分中引入了一系列简化形式的概念，这将在具体实施方式部分中进一步详细说明。本申请的发明内容部分并不意味着要试图限定出所要求保护的技术方案的关键特征和必要技术特征，更不意味着试图确定所要求保护的技术方案的保护范围。
本申请实施例第一方面提供一种生命信息处理系统，所述生命信息处理系统包括存储器、处理器和显示器，其中，所述存储器用于存储可执行程序，所述处理器用于执行所述可执行程序，使得所述处理器执行以下操作：
获取病人的多个类型的病人数据；
对所述病人数据进行分析，以得到多个分析结果，其中，所述多个分析结果至少包括从所述病人数据中提取的第一信息的第一变化趋势和第二信息的第二变化趋势；
判断所述第一变化趋势和所述第二变化趋势是否满足预设条件；
当判断所述第一变化趋势和所述第二变化趋势满足所述预设条件时，确定与所述预设条件相关的所述病人的病人状态，所述病人状态至少包括所述病人的生理系统的状态，其中，所述生理系统包括所述病人的神经系统、循环系统和呼吸系统中的至少一个；以及
控制所述显示器显示表征所述病人状态的病人状态提示信息，以及显示所述第一变化趋势和所述第二变化趋势，其中，所述病人状态提示信息至少包括对所述生理系统的状态的总体评估，并且其中，
当所述生理系统包括所述病人的神经系统时，所述第一变化趋势和所述第二变化趋势中的至少一个包括与脑神经相关的指标，
当所述生理系统包括所述病人的循环系统时，所述第一变化趋势和所述第二变化趋势中的至少一个包括与血流动力学或灌注相关的指标，
当所述生理系统包括所述病人的呼吸系统时，所述第一变化趋势和所述第二变化趋势中的至少一个包括与氧合作用相关的指标。
本申请实施例第二方面提供一种生命信息处理方法，所述方法包括：
获取病人的多个类型的病人数据；
对所述病人数据进行分析，以得到多个分析结果，其中，所述多个分析结果至少包括从所述病人数据中提取的第一信息的第一变化趋势和第二信息的第二变化趋势，所述第一信息和所述第二信息与所述病人的同一病人状态相关，所述病人状态至少包括所述病人的生理系统的状态；
判断所述第一变化趋势和所述第二变化趋势是否满足预设条件；
当判断所述第一变化趋势和所述第二变化趋势满足所述预设条件时，显示所述第一变化趋势和所述第二变化趋势。
本申请实施例第三方面提供一种生命信息处理方法，所述方法包括：
获取病人的多个类型的病人数据；
对所述病人数据进行分析，以得到多个分析结果，其中，所述多个分析结果至少包括从所述病人数据中提取的第一信息的第一变化趋势和第二信息的第二变化趋势；
判断所述第一变化趋势和所述第二变化趋势是否满足预设条件；
当判断所述第一变化趋势和所述第二变化趋势满足所述预设条件时，确
定与所述预设条件相关的所述病人的病人状态；以及
显示表征所述病人状态的病人状态提示信息，以及显示所述第一变化趋势和所述第二变化趋势。
根据本申请实施例的生命信息处理系统和生命信息处理方法通过对多个类型的病人数据进行综合分析以得到病人状态，充分考虑了多种病人数据之间的关联性，能够避免误报警、漏报警等问题，并且能够帮助医护人员快速、可靠地判断病人的病情，有利于对病人健康状态的实时掌握；在确定病人状态的过程中考虑到了病人数据的多种变化趋势的相关性，能够提高病人状态判断的准确性。
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
在附图中：
图1示出根据本申请一实施例的生命信息处理系统的示意性框图；
图2示出根据本申请一实施例的显示界面的示意图；
图3示出根据本申请一实施例的生命信息处理方法的示意性流程图；
图4示出根据本申请另一实施例的生命信息处理方法的示意性流程图；
图5示出根据本申请另一实施例的显示界面的示意图；
图6示出根据本申请又一实施例的显示界面的示意图。
为了使得本申请的目的、技术方案和优点更为明显，下面将参照附图详细描述根据本申请的示例实施例。显然，所描述的实施例仅仅是本申请的一部分实施例，而不是本申请的全部实施例，应理解，本申请不受这里描述的示例实施例的限制。基于本申请中描述的本申请实施例，本领域技术人员在没有付出创造性劳动的情况下所得到的所有其它实施例都应落入本申请的保护范围之内。
在下文的描述中，给出了大量具体的细节以便提供对本申请更为彻底的理解。然而，对于本领域技术人员而言显而易见的是，本申请可以无需一个
或多个这些细节而得以实施。在其他的例子中，为了避免与本申请发生混淆，对于本领域公知的一些技术特征未进行描述。
应当理解的是，本申请能够以不同形式实施，而不应当解释为局限于这里提出的实施例。相反地，提供这些实施例将使公开彻底和完全，并且将本申请的范围完全地传递给本领域技术人员。
在此使用的术语的目的仅在于描述具体实施例并且不作为本申请的限制。在此使用时，单数形式的“一”、“一个”和“所述/该”也意图包括复数形式，除非上下文清楚指出另外的方式。还应明白术语“组成”和/或“包括”，当在该说明书中使用时，确定所述特征、整数、步骤、操作、元件和/或部件的存在，但不排除一个或更多其它的特征、整数、步骤、操作、元件、部件和/或组的存在或添加。在此使用时，术语“和/或”包括相关所列项目的任何及所有组合。此外，本申请能够以多种不同的形式来实现，并不限于本实施例所描述的实施例。提供以下具体实施例的目的是便于对本申请公开内容更清楚透彻的理解，其中上、下、左、右等指示方位的字词仅是针对所示结构在对应附图中位置而言。术语“显示界面”可以是生命信息处理系统显示有参数波形和/或参数数值的界面；或者，可以是生命信息处理系统开机后显示的界面；或者，可以是生命信息处理系统使用频率较高的界面。
为了彻底理解本申请，将在下列的描述中提出详细的结构，以便阐释本申请提出的技术方案。本申请的可选实施例详细描述如下，然而除了这些详细描述外，本申请还可以具有其他实施方式。
目前，监护设备主要采用以下几种报警方法进行报警：一是基于单一生命体征数据的报警方法，当确定单一生命体征数据在预设时间点或预设段时间内的数值超出报警阈值时进行报警；二是基于多个生命体征数据的报警方法，当确定多个生命体征数据同时超出报警阈值时进行报警；三是基于单一生命体征数据的趋势报警方法，当确定单一生命体征数据在预设段时间内呈下降或上升趋势时进行报警。
然而，生命体征数据和其他病人数据之间通常存在关联，病人状态往往体现在多个病人数据的综合表现上。现有监护设备的报警方法未考虑这种相关关系，不仅容易出现误报警、漏报警等问题。并且，现有的监护设备只能提供病人生理状态的客观描述，对于生命体征参数的解读极大程度上依赖于
医生经验，资历相对有限的医生容易忽略多个参数的同步变化所暗含的病情恶化，耽误了病人的诊断治疗。单独的参数监护无法有效利用监护设备采集的大量生命体征参数的信息，不能挖掘出生命体征参数所传递的病人状态的变化。
此外，虽有相关方法基于机器学习等技术对多种生命体征参数进行综合分析，但一般由于机器学习等技术现阶段本身的不足，容易输出临床难以接受的异常报警，并且这类方法普遍高度依赖训练数据，在临床数据获取难度较大的现状下更加难以进行充分的模型训练，模型输出的结果难以获得临床医护人员的信赖。
针对现有监护设备的上述不足之处，本申请实施例提供了一种生命信息处理系统，用于实现状态监护。状态监护是指对病人状态进行监测，并在监测到非正常的病人状态时进行提示或报警。病人状态是对病人的整体或局部的生理机能的评估结果，反映了病人整体或局部的健康状况。并且，本申请实施例的生命信息处理系统在确定病人状态时，考虑到了病人数据的多个变化趋势之间的相关性，提高了病人数据判断的准确性。
参见图1，本申请实施例的生命信息处理系统100包括存储器110、处理器120和显示器130，其中，存储器110用于存储可执行程序，处理器120用于执行存储器110存储的可执行程序，使得处理器120执行以下操作：获取病人的多个类型的病人数据；对病人数据进行分析，以得到多个分析结果，其中，多个分析结果至少包括从病人数据中提取的第一信息的第一变化趋势和第二信息的第二变化趋势；判断第一变化趋势和第二变化趋势是否满足与同一病人状态相关的预设条件；当判断第一变化趋势和第二变化趋势满足预设条件时，显示第一变化趋势和第二变化趋势。
本申请实施例的生命信息处理系统通过对多个类型的病人数据进行综合分析，得到病人状态和引发病人状态的生理原因，并提示病人状态和生理原因，与基于参数值或参数趋势的监护方案相比，基于病人状态的监护方案充分考虑了多种病人数据之间的关联性，能够避免生命信息处理系统出现误报警、漏报警等问题，并且医护人员能够根据病人状态信息快速、可靠地判断病人的病情，有利于对病人健康状态的实时掌握。
本申请实施例的生命信息处理系统100包括但不限于监护仪、本地中央站、远程中央站、云端服务系统、移动终端中的任意一个或其组合。生命信
息处理系统100可以为便携式生命信息处理系统、转运式生命信息处理系统、或者移动式生命信息处理系统等。
在一个实施例中，生命信息处理系统100可以为监护仪，监护仪用于对病人的监测参数进行实时监测，监护仪可包括床边监护仪、穿戴式监护仪等。监护仪可以包括呼吸机监护仪、麻醉监护仪、除颤监护仪、颅内压监护仪、心电监护仪等。
生命信息处理系统100也可以包括中央站，用于接收监护仪发送的监测数据，并对监测数据进行集中监护。其中，中央站可以包括本地中央站或远程中央站。中央站通过网络将一个科室或多个科室内的监护仪进行连接，以达到实时集中监护以及数据海量存储的目的。例如，中央站存储有监测数据、病人的基本信息、病史信息和诊断信息等，但不限于此。
在一些实施例中，监护仪与中央站可以通过BeneLink组成互连平台，以实现监护仪与中央站之间进行数据通讯，例如，中央站可以对监护仪监测到的监测数据进行访问。在其它一些实施例中，监护仪与中央站还可以通过通信单元建立数据连接，通信单元包括但不限于Wifi、蓝牙或移动通信的2G、3G、4G、5G等通信单元。
本申请实施例的生命信息处理系统100还可以除监护设备以外的其他设备，例如影像采集设备、治疗及支持设备、信息系统(例如CIS、HIS、交班软件、决策支持系统等等)、移动终端(例如查房车)等。
生命信息处理系统100的处理器120可以是中央处理单元(Central Processing Unit，CPU)，还可以是其它通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其它可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。处理器120是生命信息处理系统100的控制中心，利用各种接口和线路连接整个生命信息处理系统100的各个部分。
生命信息处理系统100的存储器110用于存储可执行程序，进一步地，存储器110还可以存储生命信息处理系统100所关联的病人的生命体征数据等病人数据。示例性地，存储器110可以主要包括程序存储区和数据存储区，其中，程序存储区可存储操作系统、多个功能所需的应用程序等。此外，存
储器110可以包括高速随机存取存储器，还可以包括非易失性存储器，例如硬盘、内存、插接式硬盘，智能存储卡，安全数字卡，闪存卡多个磁盘存储器件、闪存器件、或其它易失性固态存储器件。
显示器130用于为用户提供可视化的显示输出。具体地，显示器130可以用于为用户提供可视化显示界面，包括但不限于监测界面、监测参数设置界面等。示例性地，显示器130可以实现为触摸显示器，或者具有输入面板的显示器130，即显示器130可以作为输入/输出装置。
在一些实施例中，生命信息处理系统100还包括数据采集单元，例如传感器。传感器可用于连续采集病人的监测数据。其中，连续采集是指传感器每隔预设时间连续多次地测定监测数据，其中，预设时间指的是传感器返回一个监测数据所对应的最短时间。数据采集单元和处理器120之间可以通过有线通信协议或无线通信协议相连，以使数据采集单元和处理器120之间可以进行数据交互。无线通信技术包括但不限于：各代移动通信技术(2G、3G、4G及5G)、无线网络、蓝牙(Bluetooth)、ZigBee、超宽带UWB、NFC等。
具体地，数据采集单元用于采集病人的生命体征数据。在一些实施例中，数据采集单元可以独立设置于生命信息处理系统100之外，而与生命信息处理系统100可拆卸地连接。处理器120还用于对来自数据采集单元的生命体征数据进行数据处理。在一些实施例中，生命信息处理系统100也可以不包括传感器，生命信息处理系统100可以通过通信单元接收外部监测附件采集的监测数据。
生命信息处理系统100还可以包括连接于处理器120的通信单元。在一些实施例中，生命信息处理系统100可以通过通信单元与第三方设备建立数据通信。处理器120还控制通信单元获取第三方设备的数据，或者将数据采集单元采集到的生命体征数据发送至第三方设备。通信单元包括但不限于WiFI、蓝牙、NFC、ZigBee、超宽带UWB或2G、3G、4G、5G等移动通信单元。在其它一些实施例中，生命信息处理系统100还可以通过线缆与第三方设备建立连接。第三方设备包括但不限于呼吸机设备、麻醉机设备、输注泵设备、影像采集设备等医疗设备。第三方设备还可以是云端服务系统或手机、平板电脑、个人电脑等非医疗设备。
与进行参数报警的生命信息处理系统相比，本申请实施例的生命信息处理系统100所进行的是状态监护。相较于参数报警而言，状态监护在临床上
更为复杂，单独的生命体征数据难以支撑病人状态的确定，因此，本申请实施例的生命信息处理系统100通过数据采集单元、通信单元等多种途径获取多个类型的病人数据，并对多个类型的病人数据进行综合分析，使得分析得到的病人状态更为准确，避免由于信息不足而得出错误的病人状态或无法得出病人状态。
在一些实施例中，生命信息处理系统100还包括连接处理器120的报警单元，用于输出报警提示，以便医护人员执行相应的救护措施。报警单元包括但不限于报警灯、报警扬声器等。报警信息还可以显示在显示器130上、通过报警灯闪烁以提示医护人员、或通过报警扬声器播放报警信息等。
为了实现用户接口和数据交换，除了显示器130之外，生命信息处理系统100还可以包括连接于处理器120的其他输入/输出装置，包括但不限于键盘、鼠标、触控显示屏、遥控器等输入设备，以及包括但不限于打印机、扬声器等输出设备。
应当理解的是，图1仅是生命信息处理系统100包括的部件的示例，并不构成对生命信息处理系统100的限定，且监控设备100可以包括比图1所示更多或更少的部件，或者组合某些部件，或者不同的部件。
在生命信息处理系统100为病人提供监护的过程中，处理器120获取病人的多个类型的病人数据，以充分确定病人状态。病人状态可以是病人的整体状态、生理系统的状态、器官的状态、生理部位的状态和组织的状态中的至少一个。由于临床上最为关注的是病人神经系统、呼吸系统和循环系统，因此，生理系统的状态可以包括神经系统的状态、呼吸系统的状态和循环系统的状态，系统、器官或组织等的状态所反映的均为对应系统、器官或组织等的宏观状态。
示例性地，病人数据至少包括病人的一项或多项生命体征数据。其中，生命体征数据包括但不限于心电、血压、脉搏血氧、呼吸、体温、心排量、二氧化碳、运动数据、视频数据、呼吸力学参数、血流动力学参数、氧代谢参数、脑电参数、双频指数及微循环参数中的至少一种。处理器120可以从数据采集单元获取生命信息处理系统100自身采集的生命体征数据，也可以通过通信单元从外部设备接收病人的生命体征数据。
处理器120获取的生命体征数据可以是与生命信息处理系统100所监护的病人相关的高精度、高采样频率的生命体征数据，例如心电、血压、呼吸、
血氧、体温、心排量等实时监测的数据，这类数据随时间变化较快，获取的频度较高，可在生命信息处理系统100的监护界面上进行实时呈现。由于采集条件不同，生命体征数据的采样频率可能存在差异，例如，血压数据分为无创血压数据和有创血压数据，其中有创血压数据是实时监测数据，而无创血压数据一般是间断性测量的数据，例如每隔30分钟测量一次无创血压，得到病人的收缩压、舒张压和平均压数据；类似的间断性测量数据还包括体温、尿量、血糖等数据。
处理器120获取生命体征数据的方式可以有多种，例如，一种方式是获取实时监测的生命体征数据，另一种方式是一次性获取病人状态监测期间的生命体征数据。除了生命信息处理系统100自身获取的生命体征数据以外，生命信息处理系统100还可以从外部的医疗设备或非医疗设备获取其他病人数据，示例性地，外部设备包括治疗设备、检查设备和第三方系统等，其中，治疗设备包括呼吸机、麻醉机、输液泵、体外循环设备等，检查设备包括超声成像设备、内窥镜设备等，第三方系统包括PACS系统(影像归档和通信系统)、LIS系统(实验室信息系统)、CIS系统(临床信息系统)等。示例性地，从外部设备获取的病人数据包括以下至少一项：呼吸机设备采集的监测数据和/或设备数据、麻醉机设备采集的监测数据和/或设备数据、输液泵设备采集的监测数据和/或设备数据、病情数据、检验数据、检查数据。生命信息处理系统100可以通过与外部设备的通信连接获取上述病人数据。
示例性地，病情数据包括病人基本信息、疾病诊断数据、治疗数据、护理数据以及电子病历数据中的至少一种。其中，病人基本信息包括年龄、体重、性别等，疾病诊断数据包括病史、诊断报告、医嘱、问诊对话等。生命信息处理系统100可以通过电子病历获取病人的病情数据，或者接收医护人员输入的病情数据。检验数据包括体外诊断设备采集的血常规检验数据、肝功能检验数据、肾功能检验数据、甲状腺检验数据、尿液检验数据、免疫检验数据、凝血检验数据、血气检验数据、便常规检验数据及肿瘤标记物检验数据中的至少一种，生命信息处理系统100可以通过医院实验室信息管理系统获取病人的检验数据。检查数据包括医学影像设备采集的数据，具体包括DR影像数据、CT影像数据、MRI影像数据、PET影像数据、超声影像数据、量表数据、体格测验数据中的至少一种，生命信息处理系统100可以从医学影像设备或影像归纳和通信系统获取检查数据。
处理器120获取的病人数据可以是一定时间范围内的医疗数据，该时间范围可以是预设的时间范围，例如24小时，也可以根据用户指令设置合适的时间范围，具体地，可以根据医疗数据的时间戳挑选一定时间范围内的病人数据。如果在该时间范围内不存在某些类型的病人数据，例如，某些实验室检查数据可能在24小时内没有检查结果，则可以选择最近获取的该类型的病人数据进行后续的数据处理。
对于一些病人数据，尤其是生命信息处理系统100的数据采集装置获取的生理数据，处理器120还可以进行预处理，具体包括滤波、异常/空值处理、数据采样对齐等操作。数据采样对齐即指对于血压等间断性测量的数据进行插值操作，以保证可以对间断性测量的数据于其他数据同步分析。
通过对病人数据进行预处理可以提取满足质量要求的病人数据进行后续的分析，尤其是对病人数据中的连续测量数据，对其进行质量分析有助于滤除受干扰产生的没有实际临床价值的数据。生命信息处理系统100获取的上述病人数据能够呈现病人多方面的信息，但从相关描述中也可以看出，与病人相关的病人数据多种多样，其中很大一部分数据随着时间不断变化，临床医护人员很难根据上述病人数据全面、高效地进行临床评估和决策。因此，本申请实施例的生命信息处理系统100根据病人数据自动确定病人状态，以充分利用病人数据，快速提取出病人数据中的有效信息。
示例性地，为了确定病人状态，处理器120可以确定病人数据满足的目标规则，从而根据目标规则与病人状态之间的对应关系确定病人状态。由于病人数据的数据类型和数据量庞杂，可以首先对病人数据进行分析，以得到一个或多个反映病人数据特征的分析结果，并根据分析结果确定病人数据满足的目标规则。分析结果可以是从病人数据中提取的量化特征或非量化的特征。示例性地，分析结果中可以包括时间信息，例如该分析结果的发生时刻、持续时间等。后续可以根据分析结果的时间信息，提取预设时间范围内的分析结果与预设规则进行比较。
示例性地，处理器120可以对单一类型的病人数据进行分析，得到分析结果，也可以对至少两个类型的病人数据进行分析，得到分析结果。在对至少两个类型的病人数据进行分析时，可以根据病人数据中携带的时间信息，取相同、相近或相邻时间的病人数据进行分析。对至少两个类型的病人数据进行分析可以包括计算至少两个类型的病人数据的最大值、最小值、平均值
或相关性等。例如在计算心率和血压的相关性时，可以选择皮尔逊、Kendall等合适的相关性计算方法，并设置心率数据和血压数据的起止时间，二者的起止时间可以完全相同，也可存在一定差异。
示例性地，从病人数据中提取的分析结果包括以下至少一项：参数数值、事件、对参数数值的二次处理的结果、对事件二次处理的结果。参数数值可以包括从传感器采集的监测数据中提取的参数数值，例如从心率数据中提取的心率值，从呼吸率数据中提取的呼吸率值，从血氧数据中提取的血氧值等。参数数值还可以包括从影像数据中提取的测量值，例如从超声图像中提取到的射血分数(EF)。参数数值还可以包括生化检验数据中提取到的生化指标值，例如动脉血氧分压(PaO2)、脑钠肽(BNP)等。参数数值还可以包括病人的身高、体重、年龄等。参数数值还可以包括对于非量化值(如精神状态)赋予的数值编码，例如用数值1表示精神萎靡等。
对参数数值的二次处理的结果包括使用数学方法得到的运算结果，例如包括方差、平均值、中位数、最值、趋势变化特征、波动率度量、平稳性描述、随机特性、形态模式、统计参量等。上述的趋势变化特征为反映一段时间内参数数值的趋势变化的特征值，该特征值可以反映参数数值的变化方向或变化快慢。例如，心率在1小时内以平均+0.3次/分钟的速度单调上升、氧合指数在1小时内以平均-0.5mmHg/分钟的速度单调下降，其中单调上升可用数值1表示，单调下降可用数值-1表示。对参数数值进行二次处理还可以包括对至少两种不同的处理结果进行的进一步的处理，例如基于心率的均值和标准差得到新的参数等。
根据病人数据得到的事件包括以下至少一项：超限报警、异常事件、临床事件。其中，超限报警为处理器120监测到参数数值超过预设的报警限而触发的报警，包括但不限于心率超限报警、血压超限报警、血氧饱和度超限报警等；其中“超过预设的报警限”可以是高于最高报警限或低于最低报警限。异常事件包括心律失常等基于病人数据的波形等特征所得到的、不属于超限报警的异常事件。临床事件包括病人数据中记载的检查事件、诊断事件、治疗事件、护理事件等。在根据病人数据得到事件时，还可以记录事件发生的时间，以便进行后续分析。临床事件能够反映病人状态，例如若病人进行了吸氧，则表示病人可能呼吸不稳，氧合情况不佳，但病情较轻，尚能自主呼吸。若使用呼吸机为病人提供辅助呼吸，则病人呼吸不稳的情况较为严重，
病人可能已失去意识、无法自主呼吸，呼吸机的采用的通气方式也能在一定程度上反映病人状态的严重程度，例如采用有创通气的病人可能比采用有无创通气的病人更为严重。使用升压药或降压药的给药治疗表示病人可能循环不稳，因此使用药物维持血压平稳。维持血液容量足够的补血治疗(包括但不限于补充新鲜血浆和红细胞)表示病人可能循环不稳，因此通过补血维持血压平稳。维持液体平衡的补液治疗(包括但不限于晶体补液和胶体补液)表示病人可能循环不稳，因此通过补液维持体内压力平衡。
对事件二次处理的结果包括事件的发生频率/频次、发生频率/频次的变化趋势、持续时间等信息。事件的发生频率例如包括过去4小时内心率值超限的次数、过去两小时发生房颤的次数、最近30分钟内发生室性心动过速的次数等；发生频率的变化趋势例如包括过去4小时心率值超限的次数与此前4小时内心率值超限的次数相比增加或减小、过去2小时的房颤负荷与此前两小时的房颤负荷相比增大或减小等；持续时间例如包括颅内压(ICP)过高或过低的持续时间、呼气末二氧化碳(etCO2)过高或过低的持续时间、平均动脉压(MAP)<65mmHg的持续时间等。
示例性地，在对参数数值或事件进行二次处理时，还需设置时间范围，从而将时间范围内的参数数据或事件进行二次处理。例如计算心率均值时，除了设置统计学上用于计算均值的计算方法外，还需设置时间范围，并提取时间范围内的心率值用于计算均值。计算所得的均值为心率在该时间范围内对应的分析结果。即对于每种病人数据，可以获取不同时间的分析结果，后续可以根据分析结果与时间的对应关系，对相同或相近时间的分析结果进行后续处理。
通过对多种病人数据中包含的信息进行整合和提取，得到了大量的分析结果，之后，生命信息处理系统100可以根据规则库对分析结果进行匹配和判断，从而根据分析结果确定病人状态。规则库中包含有大量的预设规则，预设规则与病人状态之间具有预先建立的对应关系，预设规则可以基于指南规则、临床共识、临床调研等多种方式而制定，与基于机器学习模型确定病人状态相比，根据预设规则确定病人状态的准确性更高，确定的结果更可控，也更符合医护人员的临床认知。
具体地，处理器120根据一个或多个分析结果，确定一个或多个分析结果所满足的一条或多条目标规则。其中，每个分析结果满足一条目标规则，
或者，多个分析结果满足一条目标规则，或者，每个分析结果满足多条目标规则，具体取决于目标规则与分析结果之间的预先设定的对应关系。分析结果满足某条目标规则，可以是分析结果完全满足该目标规则，也可以是分析结果最接近于该目标规则。
示例性地，与病人的神经系统相关的目标规则包括与脑神经相关的指标，与病人的循环系统相关的目标规则包括与血流动力学或灌注相关的指标，与病人的呼吸系统相关的目标规则包括与氧合作用相关的指标。当确定病人神经系统的状态时，处理器120可以选择与脑神经相关的病人数据，提取分析结果，并确定分析结果满足的与脑神经相关的指标。当确定病人循环系统的状态时，处理器120可以选择与血流动力学或灌注相关的病人数据，提取分析结果，并确定分析结果满足的与血流动力学或灌注相关的指标。当确定病人呼吸系统的状态时，处理器120可以选择与氧合作用相关的病人数据，提取分析结果，并确定分析结果满足的与氧合作用相关的指标。
进一步地，处理器120可以将一个或多个分析结果与规则库中的一条或多条预设规则进行比较，从而在一条或多条预设规则中确定一个或多个分析结果所满足的一条或多条目标规则。一条或多条预设规则可以存储于存储器110中，处理器120从存储器110中调用预设规则。一条或多条预设规则也可以存储在服务器中，处理器120从服务器中调用预设规则。
每条预设规则中包含针对一个或多个分析结果的一条或多条预设条件。具体地，每条预设规则中可以包括针对单个分析结果的预设条件，也可以包括针对多个分析结果的多条预设条件。预设条件可以包括阈值条件、趋势条件、定性条件等。每条预设规则定义有对应的分析结果，可以将预设规则与分析结果的对应关系选择纳入预设规则进行比较的分析结果。例如，对于“平均心率大于90”这一预设规则，选择“平均心率”这一分析结果与之进行比较。示例性地，每条预设规则还定义有持续时间，例如对于“过去4小时平均心率大于90的次数大于8次”这一预设规则，选择“过去4小时”这一时间范围，“平均心率大于90的次数”这一分析结果，确定过去4小时这一时间范围内的平均心率大于90的次数是否大于8次，若大于8次，说明该条预设规则得到了满足，即该条预设规则为目标规则；反之则说明该条预设规则未得到满足，即该条预设规则并非目标规则。对于“近30分钟内发生室性心动过速的次数超过5次”这一预设规则，选择“近30分钟内”这一时间范围内，“发生室性心动过速的次数”这一分析结果，确定近30分钟内发生室性
心动过速的次数是否超过5次，若超过5次，说明该条预设规则得到了满足，反之则该条预设规则未得到满足。
当同一条预设规则对应于至少两个分析结果时，至少两个分析结果可以是从同一种病人数据中提取到的，例如，同一条预设规则可以包括针对心率平均值的预设条件和针对心率标准差的预设条件；至少两个分析结果也可以是从不同病人数据中提取到的，例如，同一条预设规则可以包括针对心率平均值的预设条件和针对血氧平均值的预设条件。
同一预设规则中的至少两个分析结果可以是参数数值(或对参数数值二次处理的结果)与事件(或对事件二次处理的结果)的组合。例如，同一预设规则中的至少两个分析结果可以是参数数值与临床事件的组合，从而反映提供临床治疗(例如呼吸机辅助呼吸、用药、补液、补血等)后，病人病情的变化情况，用于判断治疗效果，确定病人状态的发展趋势。例如，预设规则1为发生呼吸机辅助呼吸的治疗事件后，SpO2逐渐升高，表明呼吸系统状态好转；预设规则2为使用止痛药止痛后，etCO2过低时间过长，或RR过低时间过长，或SpO2过低时间过长，说明止痛药可能过量，对呼吸系统产生抑制，需要减少药物用量。在一些实施例中，同一预设规则也可以只对应一个分析结果。例如，当已经使用呼吸机为病人提供呼吸支持时，可以认为该病人已经是非常危重的病人，因此“使用呼吸机”可以单独作为一条预设规则，对应的病人状态为无法自主呼吸。
受到病人状态变化的影响而发生变化的病人数据通常不止一种，目前的生命信息处理系统可能会对单一病人数据的变化趋势进行监测，但没有考虑到多种病人数据的变化趋势之间的相关性。单一病人数据的不显著的变化趋势可能无法单独反映出病人状态，并且也无法引起医护人员的注意，使得其中携带的信息不能得到有效利用。而多种病人数据同时呈现出某种特定的变化趋势则能够较为准确地反映病人状态，因此，本申请实施例的生命信息处理系统100对多个变化趋势进行综合考虑，从而确定病人的病人状态。
具体地，至少一条预设规则中纳入多个变化趋势以进行综合考虑，多个变化趋势至少包括从病人数据中提取的第一信息的第一变化趋势和第二信息的第二变化趋势。第一信息和第二信息可以是从病人数据中提取的参数数值、对参数数值二次处理的结果、从病人数据中提取的事件或对事件二次处理的结果；第一信息和第二信息可以是从相同的一种或多种病人数据中提取到的，
也可以是从不同的病人数据中提取到的。第一变化趋势和第二变化趋势可以是上升趋势、下降趋势、波动趋势、突变趋势等；第一变化趋势和第二变化趋势也可以是多种变化趋势的组合，例如先上升再下降、先下降再波动等。
例如，以第一信息为心率值、第一变化趋势为心率值的变化趋势(以下称为心率变化趋势)、第二信息为血氧值，第二变化趋势为血氧值的变化趋势(以下称为血氧变化趋势)为例，在从心率数据中提取到心率值后，确定心率值在第一时间范围内的变化趋势，例如确定心率值在第一时间范围内呈现上升趋势；以及，在从血氧数据中提取到血氧值后，确定血氧值在第二时间范围内的变化趋势，例如确定血氧值在第二时间范围内呈现下降趋势。其中，第一时间范围和第二时间范围可以完全相同、至少部分相同、或者相邻。确定变化趋势的方式可以有多种，以心率变化趋势为例，可以对第一时间范围内的心率值进行线性拟合，根据拟合得到的直线的斜率的大小来判断心率变化趋势；或者取不同时间窗口内的心率值求平均，通过比较平均值的大小来判断心率变化趋势等；本申请实施例对变化趋势的计算方式不做限制。
在获得心率变化趋势和血氧变化趋势后，可以将心率变化趋势和血氧变化趋势与预设规则中包含的预设条件进行比较，以判断心率变化趋势和血氧变化趋势相结合是否能够反映某种病人状态。其中，用于与心率变化趋势和血氧变化趋势进行比较的预设条件可以是同一项预设规则中所包含的至少两个预设条件。该预设规则至少定义了预设的心率变化趋势，以及预设的血氧变化趋势，将心率变化趋势和血氧变化趋势进行比较至少包括判断实际的心率变化趋势与预设的心率变化趋势是否一致，以及实际的血氧变化趋势与预设的血氧变化趋势是否一致。病人的生理参数等数据很少单独发生变化，而是多为协同变化，同时对多个变化趋势进行分析判断更符合生理规律，有助于提高病人状态识别的准确性。
例如，在与循环状态发生恶化相关联的预设规则中，预设的心率变化趋势为上升趋势，预设的血氧变化趋势为下降趋势，则若实际的心率变化趋势为上升趋势、实际的血氧变化趋势为下降趋势，则满足该预设规则；若实际的心率变化趋势和实际的血氧变化趋势均为上升趋势或均为下降趋势，或心率变化趋势为下降趋势、血氧变化趋势为上升趋势，则不满足该预设规则；心率变化趋势和血氧变化趋势的其它组合也可能表征其它病人状态。预设规则也可以定义心率变化趋势和血氧变化趋势为同向变化趋势，则心率变化趋
势和血氧变化趋势均为上升趋势或均为下降趋势，都满足该预设规则。
进一步地，预设条件还包括心率变化趋势和血氧变化趋势具有预设的时间关联性。其中，时间关联性可以包括心率变化趋势和血氧变化趋势的发生时间至少部分相同，表明心率和血氧协同变化；或者，时间关联性可以包括心率变化趋势和血氧变化趋势的发生时间之间的时间差不超过预设的时间差，例如血氧变化趋势在心率变化趋势之后的5分钟内发生，表明心率和血氧的变化具有一定的关联性；或者，时间关联性还可以包括心率变化趋势和血氧变化趋势均发生在同一更大的时间范围内。可选地，考虑到病人数据的变化可能具有一定的因果关系，预设条件还可以定义心率变化趋势和血氧变化趋势的先后关系，例如血氧变化趋势需在心率变化趋势之前发生，否则即使二者的发生时间有重叠，也不满足预设条件。当然，预设条件也可以不对先后关系进行限定。
在确定病人数据的分析结果所满足的一条或多条目标规则后，处理器120确定与目标规则相对应的病人的一项或多项病人状态，该病人状态即生命信息处理系统100针对当前监护的病人所确定的病人状态。处理器120可以根据预先设定的目标规则与病人状态之间的对应关系，将与目标规则对应的病人状态作为病人状态的确定结果。处理器120确定的病人状态通常为异常状态，原因在于异常状态更需要医护人员关注；但病人状态也可以是正常状态。例如，针对循环系统，处理器120可以根据目标规则确定病人状态为循环不稳状态，当未确定到循环不稳状态时均默认循环系统为循环稳定状态；处理器120也可以根据一些目标规则确定病人状态为循环稳定状态。
本申请实施例的病人状态可以是病人未来状态的预测结果，也可以是病人当前状态的判断结果。下文中的“循环不稳定”等病人状态可以是指病人当前循环不稳定，也可以是病人未来有可能发生循环不稳定的问题。病人未来状态的预测结果代表了病人状态的发展趋势，呈现病人未来状态的预测结果有助于医护人员提早干预，避免或减缓病人状态发生恶化。呈现病人当前状态的判断结果也有助于医护人员及时进行针对性治疗。
以往的监护设备只能对单个生理参数进行监测，对于缺乏经验的医护人员来说无法理解生理参数超限报警的原因。并且，对于重症病人来说，频繁报警可能造成医护人员报警疲劳，无法关注到病人状态的恶化。相比而言，本申请实施例的生命信息处理系统100能够基于病人数据确定病人状态，由
基于生命信息处理系统100对多种病人数据进行汇总，无需医护人员对大量数据进行主观分析，为医护人员直接提供最清晰简明、也是最需要关注的信息。当输出病人未来状态的预测结果时，更能在病人状态出现恶化的征兆时迅速阻截治疗，及早防止病情恶化。
示例性地，处理器120可以根据预设规则与预设病人状态之间的对应关系，确定与一条或多条目标规则相对应的一项或多项病人状态。预设规则与预设病人状态之间的对应关系可以存储在存储器110中，处理器120在预设规则中确定与分析结果匹配的目标规则后，根据存储器110中存储的预设规则与病人状态之间的对应关系，确定与目标规则对应的一项或多项病人状态。
其中，每条目标规则对应一项病人状态，即满足一条目标规则，则判定病人具有与该目标规则对应的一项病人状态；或者，每条目标规则对应多项病人状态，即只要满足一条目标规则，则判定病人同时具有多项病人状态；或者，多条目标规则对应一项病人状态，即只有同时满足多条目标规则时，才能够判定病人具有与该多条目标规则对应的病人状态。其中，病人状态可以包括病人多个生理结构的状态，在一些实施例中，生理结构至少包括病人的生理系统，并且生理包括临床上医护人员最为关注的循环系统、呼吸系统和神经系统。此外，生理系统还可以包括运动系统、内分泌系统、消化系统、泌尿系统、生殖系统等。生理系统的状态为对生理系统总体情况的直接反映。由于人体的生理系统由多个组织或器官构成，以往的监护设备无法对生理系统的总体情况进行评估，而本申请实施例的生命信息处理系统100综合多种类、多来源的病人数据，基于规则库实现了对生理系统状态的全面的、概括性的评估。
当病人的生理系统包括神经系统时，目标规则包括病人脑神经相关的指标，当病人的生理系统包括循环系统时，目标规则包括病人的血流动力学相关的或灌注相关的指标。当病人的生理系统包括呼吸系统时，目标规则包括病人的氧合作用相关的指标。
此外，病人状态还可以包括病人的整体状态、生理系统的状态、器官的状态、生理部位的状态、组织的状态等。其中，器官包括大脑、心脏、肺、肝脏、胃以及肾脏中的至少一个。生理部位包括头部、胸部、腹部等；组织包括肌肉组织、神经组织、上皮组织等。
病人状态可以包括病人整体、生理系统、器官、生理部位、组织中的一
个或多个的临床上定义的宏观状态，反映了该部分的整体生理机能，例如循环系统的循环不稳、灌注不足等，不同生理结构对应的病人状态可以视为对该生理结构做出的版块小结。
病人状态具体可以包括系统、器官等生理结构的恶化状态，例如该生理结构当前是否正在发生恶化，或者未来是否可能发生恶化等。病人状态还可以包括系统、器官等生理结构的异常与否、异常级别、危重等级、护理等级等。例如，循环系统的状态可以包括循环异常或循环正常，循环异常可以进一步包括循环轻微异常或循环严重异常。可选地，病人状态还可以包括与病人整体、病人的生理系统、器官、生理部位或组织相关的具体的疾病，例如急性呼吸窘迫综合征(ARDS)、呼吸衰竭、急性肾损伤(AKI)、脓毒症、心衰、脑损伤等。示例性地，病人状态还可以包括未知状态或疑似异常状态。病人状态可以视为生命信息处理系统100代替医护人员对病人数据进行总结分析所得到的综合性评估结果。
在一些实施例中，生命信息处理系统100可以分别确定病人不同生理结构的状态。例如，生命信息处理系统100可以为每个生理结构配置对应的病人数据类型、数据分析方式和预设规则。在获取到病人的多个类型的病人数据后，可以根据生理结构与病人数据的对应关系对病人数据进行分类，进而根据生理结构与数据分析方式和预设规则的对应关系对病人数据进行分析和判断。例如，可以根据呼吸系统与病人数据的对应关系，从多个类型的病人数据中提取与呼吸系统相关的病人数据，根据预先配置的数据分析方式得到相关的分析结果，并在与呼吸系统相关的多条预设规则中确定这些分析结果满足的目标规则，从而得到与呼吸系统相关的目标规则；最后根据目标规则与病人状态的对应关系得到呼吸系统的状态。
生命信息处理系统100可以预先配置多个生理结构，用户可以根据需要在其中选择目标生理结构，从而有针对性地查看特定生理结构的病人状态。生命信息处理系统100还可以为不同类型的病人或不同类型的用户配置不同的版块组合模板，并根据当前的病人类型或用户类型在多个生理结构中自动选择目标临床板块，从而智能化地呈现不同类型的病人或不同类型的用户所关注的病人状态。例如，针对颅脑创伤病人、老年病人、心衰病人、体外循环支持病人、急性呼吸窘迫综合征ARDS病人，分别定义符合诊疗这些病人需要关注的病人状态的版块组合模板；或者，针对心脑血管医生、呼吸治疗
师、康复管理人员，分别定义聚焦这些临床医护人员关注的病人状态的板块组合模板。生命信息处理系统100可以只确定目标生理结构对应的病人状态，或者，生命信息处理系统100可以确定多个生理结构对应的病人状态，但只呈现目标生理结构对应的病人状态。
此外，每个生理结构对应的病人数据类型也可以允许用户定制，可以根据接收到的用户指令确定每个生理结构对应的病人数据类型。用户可以考虑病人的病症(例如心衰、损伤、呼吸衰竭等)、医院设备、医院条件等因素选择目标生理结构和目标生理结构对应的病人数据类型。同时，版块组合模板不仅可以用于定制该模板的用户，用户还可以共享或者发布其所定制的模板，供其他人使用。在实际应用中，用户可以方便地从多个版块组合模板中选择当前使用的版块组合模板，多个版块组合模板可以是当前用户建立的，也可以是其他用户建立的。
在一些实施例中，处理器120还可以根据用于确定病人状态的第一机器学习模型，确定病人的病人状态。其中，第一机器学习模型可作为预设规则的辅助工具，用于提高病人状态的准确性。
示例性地，处理器120可以将病人数据或从病人数据中提取的分析结果或分析结果满足的目标规则输入到第一机器学习模型中，获取第一机器学习模型输出的第二病人状态，并根据第一机器学习模型输出的第二病人状态和基于目标规则确定的第一病人状态共同得到病人状态的最终确定结果。病人状态的最终确定结果可以包括第一病人状态和第二病人状态的并集，即只要基于规则库和基于第一机器学习模型中的任意一个确定某个病人状态，便认为病人出现了该病人状态；病人状态的最终确定结果也可以包括第一病人状态和第二病人状态的交集，即只有基于规则库和基于第一机器学习模型二者均确定某个病人状态时，才认为病人出现了该病人状态；或者也可以基于其他策略对第一病人状态和第二病人状态进行融合，得到更准确的病人状态。
或者，处理器120也可以基于第一机器学习模型从病人数据中提取分析结果，或者基于第一机器学习模型对从病人数据中提取到的分析结果进行二次处理；处理器120也可以基于第一机器学习模型对基于目标规则得到的病人状态进行二次处理等。
由于机器学习模型的训练完全依赖于训练数据库，如果训练数据库中数据量少、或阳性特征不足或特征不明显，都会降低机器学习模型的准确度。
并且，机器学习模型还存在预测结果不稳定、预测结果无法解释等问题，难以取信于医护人员。因此，本申请实施例根据预设规则确定病人状态，机器学习模型可作为辅助，提高了病人状态的准确性，并且可以目标规则解释得出病人状态的原因。
可选地，处理器120也可以使用机器学习模型独立地获得病人状态。具体地，处理器120可以将多个不同类型的病人数据输入到机器学习模型，得到机器学习模型输出的病人状态，或者，处理器120可以将从病人数据中提取的分析结果输入到机器学习模型，得到机器学习模型输出的病人状态；即机器学习模型的输入可以是原始的病人数据或从病人数据中提取的分析结果。随着人工智能技术的研究和进步，机器学习模型识别病人状态的准确性也在不断提高。
常规的机器学习模型无法解释得到病人状态的原因，为此，本申请实施例根据从病人数据中提取的分析结果确定目标规则，从而可以通过目标规则解释机器学习模型得到病人状态的原因。当处理器120控制显示器130同时显示病人状态和目标规则时，病人状态可以是根据目标规则确定的，也可以是根据机器学习模型确定的，或是结合目标规则与机器学习模型共同确定的。即使病人状态并非是根据目标规则确定的，目标规则也在一定程度上与病人状态相关。在一些实施例中，可以在根据机器学习模型确定病人状态后，从病人状态反推与该病人状态对应的目标规则，并显示目标规则。
在基于上述任意方法确定病人状态后，处理器120控制显示器130显示表征病人状态的病人状态提示信息；进一步地，处理器120还可以控制显示器130显示对得出的该病人状态的解释性说明，从而为病人状态提供支撑，增加病人状态的可信度，更好地辅助用户对病人进行诊疗。
其中，对病人状态的解释性说明可以包括一条或多条目标规则中的至少一部分。通过显示器130呈现病人状态提示信息，可以提醒医护人员及时关注病人，尤其是在病人状态异常时及时采取应对措施；显示与病人状态相关的目标规则可以告知医护人员生命信息处理系统100为何得出当前显示的病人状态，增加病人状态的可信度，另一方面也有助于医护人员及时对症治疗。处理器120可以根据预设的更新周期对显示器130显示的病人状态进行更新，预设的更新周期可以是生命信息处理系统100预设的，也可以是用户输入或更改的，其可以以分钟计、以小时计等。处理器120也可以在后台持续监测
病人状态，并在监测到病人状态发生变化时，控制显示器130更新显示的病人状态。
病人状态提示信息包括生理结构病人至少一个生理结构的状态的总体评估，其简要地总结概括了整个生理结构总体的状态，提供了关于整个生理结构的总结性信息。生理结构包括病人的生理系统、生理器官、生理部位、组织、生理系统的特征或生理器官的特征，生理系统包括运动系统、神经系统、内分泌系统、循环系统、呼吸系统、消化系统、泌尿系统以及生殖系统中的至少一个；生理器官包括大脑、心脏、肺、肝脏、胃以及肾脏中的至少一个；生理部位包括头部、胸部和腹部中的至少一个；组织包括肌肉组织、神经组织和上皮组织中至少一个；生理系统的特征或生理器官的特征包括出入量、凝血、营养、感染、血糖和医疗事件中的至少一个。
示例性地，处理器120可以控制显示器130通过文字或图形显示病人状态提示信息。可选地，文字和图形也可以结合使用。当采用文字方式时，可以预先配置与病人状态相关的字符串，字符串包括表征病人整体、生理系统、器官、生理部位或组织的字符串，以及表征具体状态的字符串，例如循环系统+可能休克/可能心衰/可能内出血、呼吸系统+可能呼吸抑制、神经系统+可能颅脑出血、泌尿系统+可能肾脏衰竭、免疫系统+可能严重感染等等。字符串可以有各种形式，只要能够体现病人状态即可，例如用于表征心衰的字符串可以是“循环系统可能心衰”、“病人可能心衰”、“病人有心衰风险”等。字符串可以由专家针对各病人状态预先设定，或者使用自然语言处理相关的方法适应当前具体的病人状态进行调整。示例性地，字符串也可以允许用户进行配置或修改。
当采用图形方式时，可以预先存储与每种病人状态相关的、能够形象地表示该病人状态的图形，并在确定病人状态后调用该图形进行显示。代表病人状态的图形可以与病人整体、生理系统、器官、生理部位或组织相对应，并通过位于图形上的或在图形附近的符号信息、颜色信息以及文字信息中的至少一项，对图形进行标记来呈现病人状态。例如，将代表循环系统的图形显示为红色表示循环系统状态异常，显示为绿色表示循环系统状态正常。用于表示病人状态的图形可以分别显示在与每个病人状态对应的显示区域，以便于医护人员分别查看，例如，用于表示循环系统状态的图形显示在与循环系统对应的显示区域，用于表示呼吸系统状态的图形显示在与呼吸系统对应
的显示区域、用于表示神经系统状态的图形显示在与神经系统对应的显示区域等。
在一些实施例中，如图5所述，用于表示病人状态的图形也可以显示在同一个人体状态指示图中，以便于医护人员总览病人状态。人体状态指示图可以是全身图、半身图等，其中可以显示多个人体器官或系统。例如，在人体状态指示图的头部位置显示用于表示神经系统状态的图形，在人体状态指示图的心脏位置显示用于表示循环系统状态的图形，在人体状态指示图的胸腔位置显示用于表示呼吸系统状态的图形，在人体状态指示图的腹部显示用于表示消化系统状态的图形等。人体状态指示图可以完整地显示多个器官或系统，也可以只显示目标生理结构对应的器官或系统、当前存在异常状态的器官或系统、或者用户选择的器官或系统等。用于表示各个系统或器官的状态的图形可以是形象地绘制出对应系统或器官的图形，同时可以用图形的颜色或动态变化来呈现对应系统或器官的状态。病人整体状态也可以通过人体图形来表示，例如，通过人体图形的边界线来表示脓毒症或全身感染等整体状态。
示例性地，处理器120可以控制显示器130将病人未来状态的预测结果与病人当前状态的判断结果进行区别化现实，以便于用户确定哪些病人状态是当前的病人状态，哪些病人状态是未来可能出现的病人状态。
一条或多条目标规则中的至少一部分可以与对应的病人状态相邻显示，以体现目标规则与病人状态间的相关性。显示一条或多条目标规则中的至少一部分可以包括显示多条目标规则中重要性程度较高的若干条目标规则，由于与病人状态相关的目标规则可能数量众多，选择重要性程度较高的若干条目标规则进行显示有助于医护人员重点关注，无需医护人员在多条规则中人为进行筛选。所显示的目标规则也可以按照重要性程度进行排序，使医护人员优先关注重要性程度较高的目标规则。在一些实施例中，还可以通过颜色、图形、文字等方式对目标规则的重要程度进行标记。
显示一条或多条目标规则中的至少一部分还可以包括显示多条目标规则中重要性程度较高的若干条预设条件。如上所述，每条目标规则中都可以包括一条或多条预设条件，最终显示的预设条件可以是每条目标规则中的一部分条件，即可以在每条规则中挑选医护人员更为关注的预设条件进行显示，而无需显示该规则中的其他预设条件。例如，一些目标规则中包括针对病人
年龄、体重、病情等的预设条件，这部分预设条件关系着针对生命体征数据的预设条件的阈值，虽然对病人状态的判断有着重要影响，但医护人员通常并不关注这类信息，因此，在显示目标规则时，可以只显示针对生命体征参数的预设条件，而不显示针对年龄、体重等信息的预设条件。当然，对最终显示的预设条件的选择标准还可以有很多，具体可以根据临床需要进行设置，本申请实施例对此不做限制。
在一些实施例中，处理器120还可以确定表征病人状态的严重程度的状态等级，并控制显示器130显示表征状态等级的状态等级提示信息。其中，处理器120可以综合多个病人状态共同确定综合性的状态等级，例如病人整体状态为代表严重恶化的等级；综合性的状态等级可用于提示医护人员哪些病人最需要关注。或者，处理器120可以分别确定每个病人状态的单独的状态等级，例如分别确定呼吸系统、神经系统、循环系统的状态的状态等级；单独的状态等级可用于提示医护人员当前病人的哪些方面最需要关注。
当病人存在某些异常的病人状态时，显示目标规则可以提示得出该病人状态的原因，但对于经验不足的医护人员来说，仍难以确定引发该病人状态的根本原因。因此，本申请实施例还确定引发病人状态的生理原因，并控制显示器130显示表征生理原因的生理原因提示信息，从而向用户提供关于病人状态的更深层次的解释性说明。其中，生理原因是基于生物医疗原理、临床经验等确定的导致当前病人状态的生理上的原因，具体可以是某种疾病或损伤(即病因)。生理原因也可以是另一方面的病人状态，例如，与生理系统的病人状态相关的生理原因可能是该生理系统中某个组织或器官的状态，例如某个组织或器官的状态恶化导致整个系统的恶化；或者，生理原因可以是另一生理系统的状态，例如一个生理系统的状态恶化导致另一个生理系统的状态恶化。总而言之，生理原因与病人状态之间具有因果关系。
示例性地，处理器120可以根据预设规则与生理原因之间的对应关系确定引发病人状态的生理原因。存储器110可以存储预设规则与预设生理原因之间的对应关系，在确定从病人数据中提取的分析结果满足的至少一条目标规则后，根据对应关系，确定与至少一条目标规则对应的引发病人状态的生理原因。其中，每条预设规则可以对应一个生理原因，或者，每条预设规则可以对应多个生理原因，或者，多条预设规则可以对应一个生理原因。
在一些实施例中，处理器120也可以根据确定生理原因的第二机器学习
模型来确定引发病人状态的生理原因，具体地，可以将病人数据、从病人数据中提取到的一个或多个分析结果或分析结果满足的目标规则输入到第二机器学习模型中，并获得第二机器学习模型输出的生理原因。
为了体现生理原因与目标规则的关联性，处理器120可以控制显示器130将目标规则中的至少一部分与生理原因提示信息进行关联显示。其中，关联显示可以包括通过特定的字符呈现生理原因与目标规则之间的相关性，或者，关联显示可以包括将具有关联性的目标规则和生理原因相邻显示或显示在同一区域。
例如，参见图2，根据病人数据和多条预设规则，处理器120判断从病人数据中得到的分析结果满足的目标规则包括如下几条：目标规则1.MAP在52-94mmhg，且22％时间小于65mmhg；目标规则2.Sl＞0.7，并且MSl＞0.9，并且ASl＞47；目标规则3.HR上升，并且SpO2下降。
基于上述目标规则与病人状态之间的对应关系，处理器120判断病人循环系统的状态为循环不稳定。基于上述目标规则与生理原因之间的对应关系，处理器120判断目标规则1对应的生理原因为存在长时间灌注不足风险；目标规则2对应的生理原因为可能灌注不足；目标规则3对应的生理原因为氧合不足。基于上述信息，处理器120控制显示器130显示表征循环不稳定的生理原因提示信息；显示目标规则1、目标规则2和目标规则3；并在每条目标规则之后显示与该目标规则对应的生理原因。
在以上描述中，处理器120控制显示器显示目标规则以提示得出病人状态的原因，除此之外，处理器120也可以控制显示器130显示用于得到病人状态所采用的、从病人数据中提取的信息中的至少一部分，来提示医护人员其得出病人状态的依据。例如，当基于第一机器学习模型确定病人状态时，可以确定并显示机器学习模型用于得到病人状态所采用的、从病人数据中提取的关键信息，例如，第一机器学习模型可以在输出病人状态的同时，输出对病人状态贡献最大的关键信息，显示该关键信息能够解释第一机器学习模型得到病人状态的原因，增加病人状态的可信度。可选地，机器学习模型还可以输出每种关键信息所做贡献的权重，显示器130可以按照权重由高到低的顺序显示各个关键信息。
示例性地，从病人数据中提取的信息可以包括上文所述的从病人数据中提取的参数数值、对参数数值二次处理的结果、事件以及对事件二次处理的
结果；除此之外，从病人数据中提取的信息还可以是用来得到病人状态所基于的某段波形图或趋势图，其呈现形式可以是直接呈现该段波形图、趋势图、其他统计图等，而可以不必以字符的形式呈现从病人数据中提取的信息。
其中，波形图反映病人数据在一个或多个周期内随时间的变化，例如，按照心脏激动的时间顺序，将体表的电位变化记录下来，形成一条连续的曲线，即为心电波形图；每个心动周期在心电波形图上均可出现相应的一组波形。类似的还包括血氧饱和度波形、呼气末二氧化碳波形等。趋势图用于反映某一个或多个病人数据随时间变化发展的趋势，相比于波形图的时间维度更长。趋势图的纵轴既可以是以一定采样率采集的绝对值，也可以是以一定采样率采集并计算而得的各个固定时间段内的平均值。趋势图可以是曲线图、直方图、条形图、箱线图、散点图、折线图之一，也可以是曲线图、直方图、条形图、箱线图、散点图、折线图中的各种组合。
在一些实施例中，趋势图可以用来反映从病人数据中识别到的事件随着时间变化的趋势，例如，随时间推移事件变化发展的发生形态、发展态势、发生率、趋势等等。其中，“发生率”可以是事件在最小单位时长内的发生频率，而发生形态、发展态势、趋势等等，则不限于发生频率，而是例如可以根据事件发生的实际情况进行的标记，例如，以房颤事件为例，每发生一次房颤事件，则用特殊图形/符号等进行一次标记，当为连续地、长时地发生的房颤事件时，可以用图形/符号等随着横轴的跨越长度来反映该次房颤事件发生的时长。同一个趋势图也可以用于呈现至少两种病人数据随时间的变化，例如，可以将心率的趋势图、脉率的趋势图、血氧的趋势图、无创血压的趋势图、有创血压的趋势图、呼吸的趋势图、体温的趋势图、每搏心输出量的趋势图、心排量的趋势图、心电图ST段、心电图QT间期、血糖、脑氧、尿量中的至少两个进行同时显示，即共用相同的时间轴，或者将时间轴对齐显示。将至少两项病人数据的趋势图同时显示，有助于联合呈现监测期间内不同病人数据的变化的相关性。在一些实施例中，处理器120还可以对病人数据进行噪声评估，以确认病人数据的数据质量，噪声评估可以帮助判断当前所得到的病人状态是否可靠，例如，在某些病人数据的数据质量过差时，不根据该病人数据确定病人状态，以防止不可信的信息输出而干扰医生的正常临床诊治；或者，可以在显示病人状态的同时呈现数据质量参数，数据质量参数反映用于得到病人状态的病人数据的数据质量，以辅助医护人员确认当
前显示的病人状态的可信程度。
如图6所示，本申请实施例的上述病人状态、目标规则和生理原因等信息可以显示在病人状态窗口610中，病人状态窗口610可以是叠加显示在常规监护界面上的窗口，可以由用户选择是否显示病人状态窗口，或者当病人状态发生异常时自动弹出病人状态窗口；或者，上述病人状态、目标规则和生理原因等信息可以显示在常规监护界面的病人状态区域中，即在常规监护界面中为病人状态、目标规则和生理原因等信息分配一个固定的显示区域。常规监护界面中显示有常规的生命体征数据的实时监测值、波形图、参数报警信息等。
可选地，如图5所示，上述病人状态、目标规则和生理原因等信息也可以显示在病人状态监护界面中，病人状态监护界面中显示有病人多个生理结构的信息，至少一个生理结构的信息包括该生理结构对应的总结性信息510。用户可以在常规监护界面和病人状态监护界面之间进行切换，例如，可以通过点击图6的常规监护界面上的控件，进入图5的病人状态监护界面；或者也可以直接在显示器上呈现病人状态监护界面。在一些实施例中，也采用输出电子版的报告或通过打印设备打印纸质的报告等方式，来给医生提供上述的信息。
示例性地，如图5所示，在病人状态监护界面中，不同生理结构的信息可以显示在不同的显示区域，例如每个生理结构对应一个独立卡片，或者不同生理结构的信息也可以通过不同颜色进行区分，或者用边界线进行区分等。至少一个生理结构对应的显示区域中显示有关于该生理结构的总结性信息。
示例性地，呼吸系统对应的显示区域中显示的从病人数据中提取的信息包括以下至少一种：氧合指数(PaO2/FiO2)、血氧饱和度(SpO2)、呼吸率(RR)、吸入氧浓度(FiO2)、呼末二氧化碳，血气分析参数、以及呼吸机或者氧疗设备的参数等。其中，血气分析参数的测量值包括乳酸(Lac)、动脉氧分压(PaO2)、动脉二氧化碳分压(PaCO2)等；呼吸机参数的测量值包括潮气量(Tv)，呼气终末正压(PEEP)，以及当前病人给氧的模式，例如，采用SIMV通气模式，采用插管还是面罩等方式给氧等。显示界面中可以显示参数的测量值和/或参数的变化趋势图，对于超过正常范围的测量值，可以给予标记进行突出显示。示例性地，还可以提供这些参数与上一次测量参数之间的变化。对于只显示参数测量值的参数，还可以响应于对参数测量值的选择指令，展示其在预设时间段内的数值和变化趋势图。
呼吸系统对应的显示区域中还显示有呼吸系统的总结性信息510。呼吸
系统的总结性信息可以包括对呼吸系统总体状态的概括性总结(例如呼吸不稳)、呼吸系统当前存在的问题(例如一过性低血氧、间歇性顺应性降低)，向用户提供的建议(例如请考虑调整通气支持模式或参数)、可能存在的风险(存在压力伤风险)等。显示界面上显示的从呼吸系统相关的数据中提取的信息与用于得到呼吸系统的状态的信息可以相同或不同。用于得到呼吸系统的状态的目标规则主要包括与病人的氧合作用相关的指标。
示例性地，在呼吸系统对应的显示区域中还显示有与呼吸系统有关的手动临床评估工具的入口，用户可以选择该入口以启用手动临床评估工具。
循环系统对应的显示区域中显示的信息主要包括从血流动力学和灌注相关的数据中提取的信息。与循环系统相关的病人数据包括休克指数、血压、心排量、乳酸(Lac)、以及与血流动力学及灌注情况相关的实验室指标和血流动力学参数等。其中，血压可以是有创血压、也可以是无创血压。实验室指标包括但不限于血红蛋白(Hb或HGB)、红细胞计数(RBC)、酸碱度(pH)、HCO3、碱剩余(BE)；血流动力学参数包括但不限于中心静脉压CVP、外周血管阻力指数SVRI、肺水指数ELWI、中心静脉血氧饱和度ScvO2。此外，与循环系统相关的病人数据还包括与循环系统相关的支持设备或者治疗设备的信息，具体包括支持设备或者治疗设备使用的治疗模式、设备的关键参数等。例如，是否使用ECMO等体外循环支持设备；是否使用球囊反驳泵IABP；是否使用血管活性药物等。
循环系统对应的显示区域中还显示有循环系统的状态，例如循环不稳、灌注不足等。显示界面上显示的从循环系统相关的数据中提取的信息与用于得到循环系统的状态的信息可以相同或不同。用于得到循环系统的状态的目标规则主要包括与血流动力学相关的或灌注相关的指标。
在神经系统对应的显示区域中显示的信息主要包括与脑神经相关的病人数据中提取的信息。示例性地，与神经系统相关的病人数据包括意识评分、脑部血压和血氧指标、与神经系统相关的临床评估结果等。临床上常用意识评分为GCS评分(格拉斯哥昏迷评分)，但是也允许用户自行定义意识评分规则。与神经系统相关的临床评估结果包括瞳孔大小的评估结果、瞳孔光反射评估结果、四肢肌力评估结果等。
神经系统对应的显示区域中还显示有神经系统的状态，显示界面上显示的从神经系统相关的数据中提取的信息与用于得到神经系统的状态的信息可以相同或不同。用于神经系统的状态的目标规则主要包括与病人脑神经相关的指标。
在心脏对应的显示区域中显示的信息主要包括心脏相关风险评估结果，例如TIMI(心肌梗塞溶栓治疗)评分。如果医疗对象进行了GRACE(全球急性冠脉综合征注册)评估，心脏相关风险评估结果也可以包括GRACE评分。此外，在心脏对应的显示区域中显示的信息还包括心率以及与心脏相关的生化指标，例如肌酸激酶同工酶(CK-MB)，肌钙蛋白(cTn)，利钠钛(NT-proBNP)等；以及心脏相关报警事件，例如，ST段抬高或者压低事件，对于有严重心律失常事件的，可以显示致命性心律失常事件信息，包括过去一段时间心律失常的发生次数等。
在肝脏对应的显示区域中显示的信息主要包括肝脏功能评估指标，例如丙氨酸转氨酶(ALT)、γ-谷氨酰基转(GGT)、总胆红素(Tbil)、直接胆红素(Dbil)、血氨(AMM)。对于有多次评估指标的，呈现最新指标与前一次指标的变化信息。用户可以定制希望查看的肝脏功能评估指标。示例性地，如果医疗对象进行了肝脏功能评估，例如得到了Child-pugh分级评分，则在肝脏版块对应的显示区域提供评分结果信息。
在肾脏对应的显示区域中显示的信息主要包括尿量和液体出入量。液体出入量包括液体入量和液体出量，其中，液体入量包括24小时总入量，以及24小时内输液泵泵入人体的液体入量。进一步地，液体入量还可以包括饮食液体量等。液体出量包括24小时尿量，24小时引流液量，其他设备脱水液体量等。液体出量还可以包括出汗、排泄、呕吐、出血液体量等。
示例性地，继续参照图5，在病人状态监护界面中还显示有人体状态指示图，人体状态指示图可总览病人状态。人体状态指示图可以显示在病人状态监护界面的中心位置，但不限于此。人体状态指示图可以完整地显示多个器官或系统，也可以只显示目标生理结构对应的器官或系统、当前存在异常状态的器官或系统、或者用户选择的器官或系统等。用于表示各个系统或器官的状态的图形可以是形象地绘制出对应系统或器官的图形，同时可以用图形的颜色或动态变化来呈现对应系统或器官的状态。病人整体状态也可以通过人体图形来表示，例如，通过人体图形的边界线来表示脓毒症或全身感染等整体状态。
综上所述，本申请实施例的生命信息处理系统100对多个类型的病人数据进行综合分析以得到病人状态，充分考虑了多种病人数据之间的关联性，能够避免误报警、漏报警等问题，并且能够帮助医护人员快速、可靠地判断病人的病情，有利于对病人健康状态的实时掌握；在确定病人状态的过程中
考虑到了病人数据的多种变化趋势的相关性，能够提高病人状态判断的准确性。
下面，将参考图3描述根据本申请一个实施例的生命信息处理方法。图3是本申请实施例的生命信息处理方法300的一个示意性流程图。
如图3所示，本申请实施例的生命信息处理方法300包括如下步骤：
在步骤S310，获取病人的多个类型的病人数据；
在步骤S320，对病人数据进行分析，以得到多个分析结果，其中，多个分析结果至少包括从病人数据中提取的第一信息的第一变化趋势和第二信息的第二变化趋势；
在步骤S330，判断第一变化趋势和第二变化趋势是否满足预设条件；
在步骤S340，当判断第一变化趋势和第二变化趋势满足预设条件时，确定与预设条件相关的病人的病人状态；以及
在步骤S350，显示表征病人状态的病人状态提示信息，以及显示第一变化趋势和第二变化趋势。
示例性地，预设条件包括：第一变化趋势为上升趋势、下降趋势或波动趋势；第二变化趋势为上升趋势、下降趋势或波动趋势；第一变化趋势和第二变化趋势具有预设的时间关联性。进一步地，可以显示该时间关联性。
示例性地，病人状态包括病人的整体状态、生理系统的状态、器官的状态、生理部位的状态和组织的状态中的至少一个。其中，生理系统包括病人的神经系统、循环系统和呼吸系统中的至少一个；当生理系统包括病人的神经系统时，第一变化趋势和第二变化趋势中的至少一个包括与病人脑神经相关的指标，当生理系统包括病人的循环系统时，第一变化趋势和第二变化趋势中的至少一个包括病人的与血流动力学相关的或灌注相关的指标，当生理系统包括病人的呼吸系统时，第一变化趋势和第二变化趋势中的至少一个包括病人的与氧合作用相关的指标。
如图4所示，本申请实施例另一方面提供一种生命信息处理方法400，包括如下步骤：
在步骤S410，获取病人的多个类型的病人数据，所述病人数据至少包括所述病人的一项或多项生命体征数据；
在步骤S420，对所述病人数据进行分析，以得到多个分析结果，其中，所述多个分析结果至少包括从所述病人数据中提取的第一信息的第一变化趋势和第二信息的第二变化趋势，所述第一信息和所述第二信息与所述病人的
同一病人状态相关，所述病人状态至少包括所述病人的生理系统的状态；
在步骤S430，判断所述第一变化趋势和所述第二变化趋势是否满足预设条件；
在步骤S440，当判断所述第一变化趋势和所述第二变化趋势满足所述预设条件时，显示所述第一变化趋势和所述第二变化趋势。
示例性地，预设条件包括：第一变化趋势为上升趋势、下降趋势或波动趋势；第二变化趋势为上升趋势、下降趋势或波动趋势。进一步地，预设条件还包括第一变化趋势和第二变化趋势具有预设的时间关联性，并且可以显示该时间关联性。
示例性地，病人的生理系统包括病人的神经系统、循环系统和呼吸系统中的至少一个；当生理系统包括病人的神经系统时，第一变化趋势和第二变化趋势中的至少一个包括与病人脑神经相关的指标，当生理系统包括病人的循环系统时，第一变化趋势和第二变化趋势中的至少一个包括病人的与血流动力学相关的或灌注相关的指标，当生理系统包括病人的呼吸系统时，第一变化趋势和第二变化趋势中的至少一个包括病人的与氧合作用相关的指标。
示例性地，病人状态还包括病人的整体状态、器官的状态、生理部位的状态和组织的状态中的至少一个。
本实施例的生命信息处理方法300和生命信息处理方法400可以实现于上文所述的生命信息处理系统100中，具体地，可以由生命信息处理系统100的处理器120执行生命信息处理方法300或生命信息处理方法400的各步骤。生命信息处理方法的具体细节可以参照上文对生命信息处理系统100进行的相关描述，在此不做赘述。
尽管这里已经参考附图描述了示例实施例，应理解上述示例实施例仅仅是示例性的，并且不意图将本申请的范围限制于此。本领域普通技术人员可以在其中进行各种改变和修改，而不偏离本申请的范围和精神。所有这些改变和修改意在被包括在所附权利要求所要求的本申请的范围之内。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的几个实施例中，应该理解到，所揭露的设备和方法，
可以通过其它的方式实现。例如，以上所描述的设备实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个设备，或一些特征可以忽略，或不执行。
在此处所提供的说明书中，说明了大量具体细节。然而，能够理解，本申请的实施例可以在没有这些具体细节的情况下实践。在一些实例中，并未详细示出公知的方法、结构和技术，以便不模糊对本说明书的理解。
类似地，应当理解，为了精简本申请并帮助理解各个发明方面中的一个或多个，在对本申请的示例性实施例的描述中，本申请的各个特征有时被一起分组到单个实施例、图、或者对其的描述中。然而，并不应将该本申请的方法解释成反映如下意图：即所要求保护的本申请要求比在每个权利要求中所明确记载的特征更多的特征。更确切地说，如相应的权利要求书所反映的那样，其发明点在于可以用少于某个公开的单个实施例的所有特征的特征来解决相应的技术问题。因此，遵循具体实施方式的权利要求书由此明确地并入该具体实施方式，其中每个权利要求本身都作为本申请的单独实施例。
本领域的技术人员可以理解，除了特征之间相互排斥之外，可以采用任何组合对本说明书(包括伴随的权利要求、摘要和附图)中公开的所有特征以及如此公开的任何方法或者设备的所有过程或单元进行组合。除非另外明确陈述，本说明书(包括伴随的权利要求、摘要和附图)中公开的每个特征可以由提供相同、等同或相似目的的替代特征来代替。
此外，本领域的技术人员能够理解，尽管在此所述的一些实施例包括其它实施例中所包括的某些特征而不是其它特征，但是不同实施例的特征的组合意味着处于本申请的范围之内并且形成不同的实施例。例如，在权利要求书中，所要求保护的实施例的任意之一都可以以任意的组合方式来使用。
本申请的各个部件实施例可以以硬件实现，或者以在一个或者多个处理器上运行的软件模块实现，或者以它们的组合实现。本领域的技术人员应当理解，可以在实践中使用微处理器或者数字信号处理器(DSP)来实现根据本申请实施例的一些模块的一些或者全部功能。本申请还可以实现为用于执行这里所描述的方法的一部分或者全部的装置程序(例如，计算机程序和计算机程序产品)。这样的实现本申请的程序可以存储在计算机可读介质上，或者可以具有一个或者多个信号的形式。这样的信号可以从因特网网站上下载
得到，或者在载体信号上提供，或者以任何其他形式提供。
应该注意的是上述实施例对本申请进行说明而不是对本申请进行限制，并且本领域技术人员在不脱离所附权利要求的范围的情况下可设计出替换实施例。在权利要求中，不应将位于括号之间的任何参考符号构造成对权利要求的限制。本申请可以借助于包括有若干不同元件的硬件以及借助于适当编程的计算机来实现。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过同一个硬件项来具体体现。单词第一、第二、以及第三等的使用不表示任何顺序。可将这些单词解释为名称。
以上所述，仅为本申请的具体实施方式或对具体实施方式的说明，本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。本申请的保护范围应以权利要求的保护范围为准。
Claims (42)
- 一种生命信息处理系统，其特征在于，所述生命信息处理系统包括存储器、处理器和显示器，其中，所述存储器用于存储可执行程序，所述处理器用于执行所述可执行程序，使得所述处理器执行以下操作：获取病人的病人数据；对所述病人数据进行分析，以得到多个分析结果，其中，所述多个分析结果至少包括从所述病人数据中提取的第一信息的第一变化趋势和第二信息的第二变化趋势；判断所述第一变化趋势和所述第二变化趋势是否满足预设条件；当判断所述第一变化趋势和所述第二变化趋势满足所述预设条件时，确定与所述预设条件相关的所述病人的病人状态，所述病人状态至少包括所述病人的生理系统的状态，其中，所述生理系统包括所述病人的神经系统、循环系统和呼吸系统中的至少一个；以及控制所述显示器显示表征所述病人状态的病人状态提示信息，以及显示所述第一变化趋势和所述第二变化趋势，其中，所述病人状态提示信息至少包括对所述生理系统的状态的总体评估，并且其中，当所述生理系统包括所述病人的神经系统时，所述第一变化趋势和所述第二变化趋势中的至少一个包括与脑神经相关的指标，当所述生理系统包括所述病人的循环系统时，所述第一变化趋势和所述第二变化趋势中的至少一个包括与血流动力学或灌注相关的指标，当所述生理系统包括所述病人的呼吸系统时，所述第一变化趋势和所述第二变化趋势中的至少一个包括与氧合作用相关的指标。
- 根据权利要求1所述的生命信息处理系统，其特征在于，所述预设条件包括：所述第一变化趋势为上升趋势、下降趋势或波动趋势；所述第二变化趋势为上升趋势、下降趋势或波动趋势。
- 根据权利要求2所述的生命信息处理系统，其特征在于，所述预设条件还包括所述第一变化趋势和所述第二变化趋势具有预设的时间关联性。
- 根据权利要求3所述的生命信息处理系统，其特征在于，所述处理器还执行操作：控制所述显示器显示所述时间关联性。
- 根据权利要求1至4中任一项所述的生命信息处理系统，其特征在于，所述存储器还用于存储一条或多条预设规则，以及存储所述预设规则与预设病人状态之间的对应关系，至少一条所述预设规则中包含所述预设条件；所述确定与所述预设条件相对应的所述病人的病人状态，包括：判断所述分析结果是否满足所述一条或多条预设规则，将所满足的预设规则作为目标规则；根据所述目标规则和所述对应关系，确定所述病人状态。
- 根据权利要求5所述的生命信息处理系统，其特征在于，所述处理器还控制所述显示器显示所述目标规则中的至少一部分。
- 根据权利要求1所述的生命信息处理系统，其特征在于，所述确定与所述预设条件相关的所述病人的病人状态，包括：将所述分析结果输入到用于确定病人状态的第一机器学习模型中，获得所述第一机器学习模型输出的病人状态。
- 根据权利要求1至7中任一项所述的生命信息处理系统，其特征在于，所述病人状态还包括所述病人的整体状态、器官的状态、生理部位的状态和组织的状态中的至少一个。
- 根据权利要求1至8中任一项所述的生命信息处理系统，其特征在于，所述器官包括大脑、心脏、肺、肝脏、胃以及肾脏中的至少一个。
- 根据权利要求1至9中任一项所述的生命信息处理系统，其特征在于，所述病人状态为所述病人未来状态的预测结果和/或所述病人当前状态的判断结果。
- 根据权利要求1至10中任一项所述的生命信息处理系统，其特征在于，所述处理器还执行以下操作：根据所述多个分析结果确定引发所述病人状态的生理原因；控制所述显示器显示与所述生理原因相关的信息。
- 根据权利要求11所述的生命信息处理系统，其特征在于，所述存储器还用于存储一条或多条预设规则，以及存储所述预设规则与预设病因之间的对应关系；所述基于所述多个分析结果确定引发所述病人状态的病因，包括：判断所述分析结果是否满足所述一条或多条预设规则，将所满足的预设规则作为目标规则；根据所述目标规则和所述对应关系，确定所述生理原因。
- 根据权利要求12所述的生命信息处理系统，其特征在于，所述处理器还执行操作：控制所述显示器将所述目标规则中的至少一部分与所述生理原因进行关联显示。
- 根据权利要求11所述的生命信息处理系统，其特征在于，所述基于所述多个分析结果确定引发所述病人状态的生理原因，包括：将所述多个分析结果输入到用于确定引发所述病人状态的生理原因的第二机器学习模型中，基于所述第二机器学习模型确定引发所述病人状态的生理原因。
- 根据权利要求1-14中任一项所述的生命信息处理系统，其特征在于，所述分析结果包括以下至少一项：参数数值、事件、对参数数值的二次处理的结果、对事件二次处理的结果。
- 根据权利要求15所述的生命信息处理系统，其特征在于，所述事件包括以下至少一项：超限报警、异常事件、临床事件。
- 根据权利要求1-16中任一项所述的生命信息处理系统，其特征在于，所述病人的生命体征数据包括心电、血压、脉搏血氧、呼吸、体温、心排量、二氧化碳、运动数据、视频数据、呼吸力学参数、血流动力学参数、氧代谢参数、脑电参数、双频指数及微循环参数中的至少一种。
- 根据权利要求1-17中任一项所述的生命信息处理系统，其特征在于，所述病人数据还包括以下至少一项：呼吸机设备采集的监测数据和/或设备数据、麻醉机设备采集的监测数据和/或设备数据、输液泵设备采集的监测数据和/或设备数据、病情数据、检验数据、检查数据。
- 根据权利要求18所述的生命信息处理系统，其特征在于，所述病情数据包括患者基本信息、疾病诊断数据、治疗数据、护理数据以及电子病历数据中的至少一种；所述检验数据包括生化检验指标数据，所述生化检验指标数据包括血常规检验数据、肝功能检验数据、肾功能检验数据、甲状腺检验数据、尿液检验数据、免疫检验数据、凝血检验数据、血气检验数据、便常规检验数据及肿瘤标记物检验数据中的至少一种；所述检查数据包括DR影像数据、CT影像数据、MRI影像数据、PET影像数据、超声影像数据、量表数据、体格测验数据中的至少一种。
- 一种生命信息处理方法，其特征在于，所述方法包括：获取病人的多个类型的病人数据；对所述病人数据进行分析，以得到多个分析结果，其中，所述多个分析结果至少包括从所述病人数据中提取的第一信息的第一变化趋势和第二信息的第二变化趋势，所述第一信息和所述第二信息与所述病人的同一病人状态相关；判断所述第一变化趋势和所述第二变化趋势是否满足预设条件；当判断所述第一变化趋势和所述第二变化趋势满足所述预设条件时，显示所述第一变化趋势和所述第二变化趋势。
- 根据权利要求20所述的方法，其特征在于，所述预设条件包括：所述第一变化趋势为上升趋势、下降趋势或波动趋势；所述第二变化趋势为上升趋势、下降趋势或波动趋势。
- 根据权利要求21所述的方法，其特征在于，所述预设条件还包括所述第一变化趋势和所述第二变化趋势具有预设的时间关联性。
- 根据权利要求22所述的方法，其特征在于，所述方法还包括：显示所述时间关联性。
- 根据权利要求20至23中任一项所述的方法，其特征在于，还包括：确定与所述预设条件相关的所述病人的病人状态。
- 根据权利要求24所述的方法，其特征在于，还包括：显示表征所述病人状态的病人状态提示信息
- 根据权利要求24或25所述的方法，其特征在于，所述确定与所述预设条件相对应的所述病人的病人状态，包括：将所述分析结果与所述一条或多条预设规则进行比较，在所述一条或多条预设规则中确定所述分析结果满足的一条或多条目标规则；根据所述目标规则和所述对应关系，确定所述病人状态。
- 根据权利要求26所述的方法，其特征在于，还包括：显示所述一条或多条目标规则中的至少一部分。
- 根据权利要求24或25所述的方法，其特征在于，所述确定与所述预设条件相关的所述病人的病人状态，包括：将所述分析结果输入到用于确定病人状态的第一机器学习模型中，获得所述第一机器学习模型输出的病人状态。
- 根据权利要求20至28中任一项所述的方法，其特征在于，所述病人状态还包括所述病人的整体状态、生理系统的状态、器官的状态、生理部位的状态和组织的状态中的至少一个。
- 根据权利要求25至29中任一项所述的方法，其特征在于，所述显示表征所述病人状态的病人状态提示信息，包括：通过文字或图形呈现所述病人状态。
- 根据权利要求29所述的方法，其特征在于，所述生理系统包括所述病人的神经系统、循环系统、呼吸系统、运动系统、内分泌系统、消化系统、泌尿系统和生殖系统中的至少一个。
- 根据权利要求31所述的方法，其特征在于，当所述生理系统包括所述病人的神经系统时，所述第一变化趋势和所述第二变化趋势中的至少一个包括与病人脑神经相关的指标，当所述生理系统包括所述病人的循环系统时，所述第一变化趋势和所述第二变化趋势中的至少一个包括病人的与血流动力学相关的或灌注相关的指标，当所述生理系统包括所述病人的呼吸系统时，所述第一变化趋势和所述第二变化趋势中的至少一个包括病人的与氧合作用相关的指标。
- 根据权利要求29至32中任一项所述的方法，其特征在于，所述器官包括大脑、心脏、肺、肝脏、胃以及肾脏中的至少一个。
- 根据权利要求20-33中任一项所述的方法，其特征在于，所述病人数据包括所述病人的生命体征数据，所述生命体征数据包括心电、血压、脉搏血氧、呼吸、体温、心排量、二氧化碳、运动数据、视频数据、呼吸力学参数、血流动力学参数、氧代谢参数、脑电参数、双频指数及微循环参数中的至少一种。
- 根据权利要求20-34中任一项所述的方法，其特征在于，所述病人数据包括以下至少一项：呼吸机设备采集的监测数据和/或设备数据、麻醉机设备采集的监测数据和/或设备数据、输液泵设备采集的监测数据和/或设备数据、病情数据、检验数据、检查数据。
- 根据权利要求35所述的方法，其特征在于，所述病情数据包括病人基本信息、疾病诊断数据、治疗数据、护理数据以及电子病历数据中的至少一种；所述检验数据包括血常规检验数据、肝功能检验数据、肾功能检验数据、甲状腺检验数据、尿液检验数据、免疫检验数据、凝血检验数据、血气检验数据、便常规检验数据及肿瘤标记物检验数据中的至少一种；所述检查数据包括DR影像数据、CT影像数据、MRI影像数据、PET影像数据、超声影像数据、量表数据、体格测验数据中的至少一种。
- 根据权利要求20-36中任一项所述的方法，其特征在于，所述病人状态为所述病人未来状态的预测结果和/或所述病人当前状态的判断结果。
- 一种生命信息处理方法，其特征在于，所述方法包括：获取病人的病人数据；对所述病人数据进行分析，以得到多个分析结果，其中，所述多个分析结果至少包括从所述病人数据中提取的第一信息的第一变化趋势和第二信息 的第二变化趋势；判断所述第一变化趋势和所述第二变化趋势是否满足预设条件；当判断所述第一变化趋势和所述第二变化趋势满足所述预设条件时，确定与所述预设条件相关的所述病人的病人状态；以及显示表征所述病人状态的病人状态提示信息，以及显示所述第一变化趋势和所述第二变化趋势。
- 根据权利要求38所述的方法，其特征在于，所述预设条件包括：所述第一变化趋势为上升趋势、下降趋势或波动趋势；所述第二变化趋势为上升趋势、下降趋势或波动趋势。
- 根据权利要求39所述的方法，其特征在于，所述预设条件还包括所述第一变化趋势和所述第二变化趋势具有预设的时间关联性；所述方法还包括：显示所述时间关联性。
- 根据权利要求26-40中任一项所述的方法，其特征在于，所述病人状态还包括所述病人的整体状态、生理系统的状态、器官的状态、生理部位的状态和组织的状态中的至少一个。
- 根据权利要求41所述的方法，其特征在于，所述生理系统包括所述病人的神经系统、循环系统和呼吸系统中的至少一个；当所述生理系统包括所述病人的神经系统时，所述第一变化趋势和所述第二变化趋势中的至少一个包括与病人脑神经相关的指标，当所述生理系统包括所述病人的循环系统时，所述第一变化趋势和所述第二变化趋势中的至少一个包括病人的与血流动力学相关的或灌注相关的指标，当所述生理系统包括所述病人的呼吸系统时，所述第一变化趋势和所述第二变化趋势中的至少一个包括病人的与氧合作用相关的指标。
Applications Claiming Priority (8)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210970377.4 | 2022-08-12 | ||
| CN202210970377 | 2022-08-12 | ||
| CN2023098103 | 2023-06-02 | ||
| CNPCT/CN2023/098103 | 2023-06-02 | ||
| CN2023098138 | 2023-06-02 | ||
| CNPCT/CN2023/098116 | 2023-06-02 | ||
| CNPCT/CN2023/098138 | 2023-06-02 | ||
| CN2023098116 | 2023-06-02 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2024032809A1 true WO2024032809A1 (zh) | 2024-02-15 |
Family
ID=89851027
Family Applications (3)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CN2023/112988 WO2024032809A1 (zh) | 2022-08-12 | 2023-08-14 | 生命信息处理系统和生命信息处理方法 |
| PCT/CN2023/112989 WO2024032810A1 (zh) | 2022-08-12 | 2023-08-14 | 生命信息处理系统和生命信息处理方法 |
| PCT/CN2023/112990 WO2024032811A1 (zh) | 2022-08-12 | 2023-08-14 | 生命信息处理系统和生命信息处理方法 |
Family Applications After (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CN2023/112989 WO2024032810A1 (zh) | 2022-08-12 | 2023-08-14 | 生命信息处理系统和生命信息处理方法 |
| PCT/CN2023/112990 WO2024032811A1 (zh) | 2022-08-12 | 2023-08-14 | 生命信息处理系统和生命信息处理方法 |
Country Status (1)
| Country | Link |
|---|---|
| WO (3) | WO2024032809A1 (zh) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118071216B (zh) * | 2024-04-24 | 2024-07-23 | 南京华建检测技术有限公司 | 基于lims系统的多维度可视化不合格数据分析方法 |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101836863A (zh) * | 2009-03-19 | 2010-09-22 | 深圳迈瑞生物医疗电子股份有限公司 | 使用两通道监护患者的方法和系统 |
| US20180263575A1 (en) * | 2015-10-10 | 2018-09-20 | Shenzhen Mindray Bio-Medical Electronics Co., Ltd. | Medical monitoring system, method of displaying monitoring data, and monitoring data display device |
| CN110648737A (zh) * | 2019-08-20 | 2020-01-03 | 首都医科大学 | 基于老年照护指南和失能失智的知识库建立系统及方法 |
| CN113384241A (zh) * | 2021-07-08 | 2021-09-14 | 郑州大学第二附属医院 | 一种可穿戴设备辅助的慢性病患者临床监测平台与方法 |
| WO2022135583A1 (zh) * | 2020-12-25 | 2022-06-30 | 深圳迈瑞生物医疗电子股份有限公司 | 监护设备和用于监护设备的显示方法 |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8388530B2 (en) * | 2000-05-30 | 2013-03-05 | Vladimir Shusterman | Personalized monitoring and healthcare information management using physiological basis functions |
| US20100280350A1 (en) * | 2009-05-02 | 2010-11-04 | Xinyu Zhang | Chinese medicine tele-diagnostics and triage system |
| CN103211585A (zh) * | 2013-04-07 | 2013-07-24 | 北京海利赢医疗科技有限公司 | 多参数监护分析系统 |
| CN107358014B (zh) * | 2016-11-02 | 2021-01-26 | 华南师范大学 | 一种生理数据的临床前处理方法及系统 |
| CN110660492A (zh) * | 2018-06-28 | 2020-01-07 | 深圳市理邦精密仪器股份有限公司 | 动态生理参数监测方法、系统、监测设备及介质 |
| WO2020132827A1 (zh) * | 2018-12-24 | 2020-07-02 | 深圳迈瑞生物医疗电子股份有限公司 | 一种应用于监护设备的显示方法及监护设备 |
| CN112911992A (zh) * | 2018-12-29 | 2021-06-04 | 深圳迈瑞生物医疗电子股份有限公司 | 监护方法、监护仪及计算机存储介质 |
| CN111612278A (zh) * | 2020-06-01 | 2020-09-01 | 戴松世 | 生命状态预测方法、装置、电子设备及存储介质 |
| CN114680820A (zh) * | 2020-12-31 | 2022-07-01 | 深圳迈瑞生物医疗电子股份有限公司 | 状态报警方法、监护设备、监护系统及可读存储介质 |
-
2023
- 2023-08-14 WO PCT/CN2023/112988 patent/WO2024032809A1/zh unknown
- 2023-08-14 WO PCT/CN2023/112989 patent/WO2024032810A1/zh unknown
- 2023-08-14 WO PCT/CN2023/112990 patent/WO2024032811A1/zh unknown
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101836863A (zh) * | 2009-03-19 | 2010-09-22 | 深圳迈瑞生物医疗电子股份有限公司 | 使用两通道监护患者的方法和系统 |
| US20180263575A1 (en) * | 2015-10-10 | 2018-09-20 | Shenzhen Mindray Bio-Medical Electronics Co., Ltd. | Medical monitoring system, method of displaying monitoring data, and monitoring data display device |
| CN110648737A (zh) * | 2019-08-20 | 2020-01-03 | 首都医科大学 | 基于老年照护指南和失能失智的知识库建立系统及方法 |
| WO2022135583A1 (zh) * | 2020-12-25 | 2022-06-30 | 深圳迈瑞生物医疗电子股份有限公司 | 监护设备和用于监护设备的显示方法 |
| CN113384241A (zh) * | 2021-07-08 | 2021-09-14 | 郑州大学第二附属医院 | 一种可穿戴设备辅助的慢性病患者临床监测平台与方法 |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2024032810A1 (zh) | 2024-02-15 |
| WO2024032811A1 (zh) | 2024-02-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20220277849A1 (en) | Wellness analysis system | |
| US20220110541A1 (en) | Methods Of Non-Invasively Determining Blood Oxygen Level And Related Pulmonary Gas Exchange Information | |
| US9211096B2 (en) | Real time clinical decision support system having medical systems as display elements | |
| JP4391089B2 (ja) | 患者の状態表示 | |
| US10388405B2 (en) | Systems and methods for predicting adverse events and assessing level of sedation during medical procedures | |
| US11972843B2 (en) | Systems and methods for predicting adverse events and assessing level of sedation during medical procedures | |
| US8936555B2 (en) | Real time clinical decision support system having linked references | |
| US20180206733A1 (en) | Device, method and system for monitoring and management of changes in hemodynamic parameters | |
| US9402597B1 (en) | Mobile vascular health evaluation processes | |
| WO2017141131A1 (en) | System and method for determining a hemodynamic instability risk score for pediatric subjects | |
| JP2018531067A (ja) | 撮像及び生理学的モニタリングを組み合わせた強化型の急性ケアマネジメント | |
| JP2018531067A6 (ja) | 撮像及び生理学的モニタリングを組み合わせた強化型の急性ケアマネジメント | |
| WO2020082341A1 (zh) | 医疗设备、用于医疗设备的多工作模式监护设置方法及装置 | |
| WO2024032809A1 (zh) | 生命信息处理系统和生命信息处理方法 | |
| JP2014500100A (ja) | 人間又は動物の対象におけるボレミック状態の観察 | |
| JP2018504952A (ja) | 多種類のデータを使用して輸液反応性を評価する方法及びシステム | |
| CN111798956A (zh) | 一种用于人工智能麻醉的决策确定方法、装置及系统 | |
| US20220202350A1 (en) | Multiparameter noninvasive sepsis monitor | |
| CN116548920A (zh) | 一种基于神经网络的麻醉状态监测系统及方法 | |
| US20150265208A1 (en) | Systems and methods for pain measurement | |
| WO2014182677A1 (en) | Real time clinical decision support system having medical systems as display elements | |
| WO2014182682A1 (en) | Real time clinical decision support system having linked references | |
| CN117594180A (zh) | 显示医疗数据的方法和生命信息处理系统 | |
| US20230255498A1 (en) | Monitoring users with a mobile device while the users engage in activities | |
| US20150379220A1 (en) | Device and system communicating with a subject |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 23852028 Country of ref document: EP Kind code of ref document: A1 |