CN116805520A - Digital twinning-based sepsis patient association prediction method and system - Google Patents
Digital twinning-based sepsis patient association prediction method and system Download PDFInfo
- Publication number
- CN116805520A CN116805520A CN202311048018.4A CN202311048018A CN116805520A CN 116805520 A CN116805520 A CN 116805520A CN 202311048018 A CN202311048018 A CN 202311048018A CN 116805520 A CN116805520 A CN 116805520A
- Authority
- CN
- China
- Prior art keywords
- sepsis
- data
- patient
- detected
- detection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 206010040047 Sepsis Diseases 0.000 title claims abstract description 253
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000001514 detection method Methods 0.000 claims abstract description 237
- 238000004088 simulation Methods 0.000 claims abstract description 39
- 230000002776 aggregation Effects 0.000 claims abstract description 11
- 238000004220 aggregation Methods 0.000 claims abstract description 11
- 201000010099 disease Diseases 0.000 claims description 31
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 31
- 230000002159 abnormal effect Effects 0.000 claims description 28
- 230000005540 biological transmission Effects 0.000 claims description 12
- 230000002526 effect on cardiovascular system Effects 0.000 claims description 9
- 210000005228 liver tissue Anatomy 0.000 claims description 9
- 210000003563 lymphoid tissue Anatomy 0.000 claims description 9
- 210000005084 renal tissue Anatomy 0.000 claims description 9
- 210000001519 tissue Anatomy 0.000 claims description 9
- 230000005856 abnormality Effects 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 7
- 238000013075 data extraction Methods 0.000 claims description 7
- 238000013507 mapping Methods 0.000 claims description 7
- 238000007621 cluster analysis Methods 0.000 claims description 6
- 230000000007 visual effect Effects 0.000 claims description 6
- BPYKTIZUTYGOLE-IFADSCNNSA-N Bilirubin Chemical compound N1C(=O)C(C)=C(C=C)\C1=C\C1=C(C)C(CCC(O)=O)=C(CC2=C(C(C)=C(\C=C/3C(=C(C=C)C(=O)N\3)C)N2)CCC(O)=O)N1 BPYKTIZUTYGOLE-IFADSCNNSA-N 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 238000011160 research Methods 0.000 description 8
- DDRJAANPRJIHGJ-UHFFFAOYSA-N creatinine Chemical compound CN1CC(=O)NC1=N DDRJAANPRJIHGJ-UHFFFAOYSA-N 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 210000002966 serum Anatomy 0.000 description 6
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 238000003745 diagnosis Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000035939 shock Effects 0.000 description 4
- 102000001554 Hemoglobins Human genes 0.000 description 3
- 108010054147 Hemoglobins Proteins 0.000 description 3
- 210000004369 blood Anatomy 0.000 description 3
- 239000008280 blood Substances 0.000 description 3
- 229940109239 creatinine Drugs 0.000 description 3
- 102100036475 Alanine aminotransferase 1 Human genes 0.000 description 2
- 108010082126 Alanine transaminase Proteins 0.000 description 2
- 206010040070 Septic Shock Diseases 0.000 description 2
- 238000004820 blood count Methods 0.000 description 2
- HVYWMOMLDIMFJA-DPAQBDIFSA-N cholesterol Chemical compound C1C=C2C[C@@H](O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2 HVYWMOMLDIMFJA-DPAQBDIFSA-N 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 230000034994 death Effects 0.000 description 2
- 231100000517 death Toxicity 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000003814 drug Substances 0.000 description 2
- 210000003743 erythrocyte Anatomy 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000005012 migration Effects 0.000 description 2
- 238000013508 migration Methods 0.000 description 2
- 230000008383 multiple organ dysfunction Effects 0.000 description 2
- 230000008506 pathogenesis Effects 0.000 description 2
- 230000001991 pathophysiological effect Effects 0.000 description 2
- 230000036303 septic shock Effects 0.000 description 2
- 238000008789 Direct Bilirubin Methods 0.000 description 1
- 102000003855 L-lactate dehydrogenase Human genes 0.000 description 1
- 108700023483 L-lactate dehydrogenases Proteins 0.000 description 1
- LEHOTFFKMJEONL-UHFFFAOYSA-N Uric Acid Chemical compound N1C(=O)NC(=O)C2=C1NC(=O)N2 LEHOTFFKMJEONL-UHFFFAOYSA-N 0.000 description 1
- TVWHNULVHGKJHS-UHFFFAOYSA-N Uric acid Natural products N1C(=O)NC(=O)C2NC(=O)NC21 TVWHNULVHGKJHS-UHFFFAOYSA-N 0.000 description 1
- PNNCWTXUWKENPE-UHFFFAOYSA-N [N].NC(N)=O Chemical compound [N].NC(N)=O PNNCWTXUWKENPE-UHFFFAOYSA-N 0.000 description 1
- 230000001154 acute effect Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000003115 biocidal effect Effects 0.000 description 1
- 230000036772 blood pressure Effects 0.000 description 1
- 230000000747 cardiac effect Effects 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 235000012000 cholesterol Nutrition 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013079 data visualisation Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 210000000265 leukocyte Anatomy 0.000 description 1
- 150000002632 lipids Chemical class 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000002503 metabolic effect Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 230000008289 pathophysiological mechanism Effects 0.000 description 1
- 230000035778 pathophysiological process Effects 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000029058 respiratory gaseous exchange Effects 0.000 description 1
- 238000004062 sedimentation Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 230000002195 synergetic effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 229940116269 uric acid Drugs 0.000 description 1
- 239000002550 vasoactive agent Substances 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
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- 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
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- 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
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
-
- 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
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Data Mining & Analysis (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Pathology (AREA)
- Databases & Information Systems (AREA)
- Biomedical Technology (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention discloses a prediction method and a prediction system for association of sepsis patients based on digital twinning, which are used for acquiring first detection data of sepsis patients to be detected, and clustering the illness states of the sepsis patients to be detected to obtain illness state clustering results; acquiring second detection data of patients to be detected with sepsis under the condition aggregation sets of all patients by adopting different data detection strategies; generating a sepsis digital twin simulation model, and inputting second detection data to obtain pre-alarm data of a patient to be detected sepsis; acquiring treatment archive data of the discharged rehabilitation patient, extracting detection data of the rehabilitation patient and pre-alarm data for comparison, and obtaining an alarm comparison result; and sending risk alarm predictions to different sepsis patient terminals by adopting different alarm strategies. The invention can effectively extract and analyze data according to different strategies of the damaged part of the sepsis patient, and combines the historical rehabilitation patient data to perform early warning comparison, thereby improving the correlation predictability of the sepsis.
Description
Technical Field
The invention relates to the technical field of medical systems, in particular to a digital twin-based sepsis patient association prediction method and system.
Background
Digital Twin (DT) is a simulation process that fully utilizes data such as actual models, sensor updates, running histories, and the like, and cooperates with multiple disciplines, multiple actual amounts, multiple scales, and multiple probabilities, and completes association in a privacy space, thereby reflecting the full life cycle process of corresponding object equipment. DT may be considered a digital association system of one or more important, mutually dependent equipment systems. DT is a universally adapted theoretical technology system and can be applied in a plurality of fields, and in recent years, as DT technology is applied in medicine, it will become possible to provide personalized diagnosis and treatment for patients, which are personalized accurate medical treatment. Some previous researches take the transformation of computing physiology into clinical practice as a hope, and a digital technology is utilized to generate a privacy physiological human body, and a certain progress is made. The development of technologies such as big data, cloud computing, privacy reality, the Internet of things and the like enables us to obtain more data more easily, lays a foundation for the application of DT technology, and provides finer dimensions for clinicians and researchers to study the occurrence and development of diseases and to perform more accurate diagnosis and treatment. The model created by DT is a privacy copy of human organs, tissues, cells or microenvironment, is continuously adjusted according to the change of online data, and can predict the future of corresponding objects and indexes. Meanwhile, the DT is not only a digital model, but also a living, intelligent and continuously developed model, and the future state can be continuously predicted while optimizing the flow and algorithm according to closed-loop optimization between the DT and the surrounding environment.
Early identification and effective circulatory-respiratory treatment is an important measure of life saving, with high mortality rates of sepsis shock. However, the combined use of multiple drugs and complex ventilator parameter adjustments involved in this treatment creates an individualized optimal circulatory-respiratory combination scheme to improve clinical outcome remains an unresolved significant problem. Here we propose to generate a digital twin simulation model of sepsis shock, conduct a study of a digital twin driven sepsis shock respiratory-circulatory integrated treatment protocol. The research is based on a multi-parameter digital clinical research platform, combines multidimensional circulation-respiratory pathophysiological parameters, metabolic data and known diagnosis and treatment schemes, and establishes a multi-mode and multi-scale patient simulation and prediction model by applying the latest artificial intelligence and data science means. The full-course simulation and prediction of the combination strategy of the resuscitation fluid, the vasoactive drug and the breathing machine parameters can be realized, and the intelligent treatment suggestion based on the combination of the digital twin simulation model and the real-world clinical data is output for the reference of doctors. The real-time interactive digital twin simulation model for the complicated disease of sepsis shock is built for the first time in the world, and the model belongs to an important breakthrough innovation in the field of acute critical diseases. In addition, in mathematical model generation, using the latest technology of machine learning and graph network, mathematical approximation and modeling of unknown pathophysiological mechanisms and processes are proposed in order to discover new pathophysiological laws. In a word, the research has theoretical and methodological innovativeness, and the research mode can inspire the intervention research of other critical diseases, and has strong originality, generalization and replicability.
Disclosure of Invention
According to a first aspect of the invention, the invention claims a method for predicting sepsis patient associations based on digital twinning, comprising:
acquiring first detection data of a patient to be detected with sepsis, and clustering the illness state of the patient to be detected with sepsis according to the first detection data to obtain an illness state clustering result of the patient to be detected with sepsis;
acquiring second detection data of the sepsis patients to be detected under the condition aggregation sets of all the patients by adopting different data detection strategies according to the condition clustering results of the sepsis patients to be detected;
generating a sepsis digital twin simulation model, and inputting the second detection data into the sepsis digital twin simulation model to obtain pre-alarm data of the patient to be detected;
acquiring treatment archive data of the discharged rehabilitation patient, extracting rehabilitation patient detection data of the treatment archive data, and comparing the rehabilitation patient detection data with the pre-alarm data to obtain an alarm comparison result;
and according to the alarm comparison result, adopting different alarm strategies to send risk alarm predictions to different sepsis patient terminals.
Further, the obtaining the first detection data of the patient with sepsis to be detected, and clustering the patient with sepsis to be detected according to the first detection data to obtain a disease clustering result of the patient with sepsis to be detected, specifically includes:
Acquiring first detection data of the patient to be detected with sepsis, and extracting one or more abnormality index values detected at different parts of the patient to be detected with sepsis from the first detection data;
acquiring a first abnormal index value with the largest proportion from normal values in the abnormal index values, and identifying a detection part corresponding to the first abnormal index value;
and clustering the illness states of the patients to be detected with sepsis according to the detection parts to obtain illness state clustering results of the patients to be detected with sepsis.
Further, according to the disease condition clustering result of the patient to be detected with sepsis, acquiring second detection data of the patient to be detected with sepsis under each patient disease condition aggregation set by adopting different data detection strategies, specifically including:
acquiring a disease clustering result of the patient to be detected with sepsis, and acquiring lymphatic tissue detection data of the patient to be detected with sepsis when the patient to be detected with sepsis belongs to a first type of potential sepsis;
when the patient to be detected is in the second type of potential sepsis, acquiring liver tissue detection data of the patient to be detected;
when the patient to be detected is in a third type of potential sepsis, acquiring kidney tissue detection data of the patient to be detected;
And when the patient to be detected is in the fourth type of potential sepsis, acquiring the cardiovascular and cerebrovascular tissue detection data of the patient to be detected.
Further, the generating of the sepsis digital twin simulation model, inputting the second detection data into the sepsis digital twin simulation model, and obtaining pre-alarm data of the patient to be detected, specifically includes:
acquiring real-time operation data of the second detection data, and acquiring data comprising potential difference, detection equipment type, detection times and total detection duration according to a sensor in the detection device;
a twin correlation unit generating second detection data; generating a three-dimensional model detection data format on a U3D platform, generating a three-dimensional visual model of the detection device, and further developing data association, a digital output unit, a privacy object editing unit, a centralized management port, programmable debugging, a historical data recording unit, an expansion port and a data simulation analysis unit;
the generation resource control unit realizes corresponding resource control according to the port provided by the data association unit, and specifically comprises the following steps: unified port control resource control, programmable debug resource control, historical data record resource control, data access resource control, data cycle resource control, and detection device sepsis management resource control;
Mapping among the units is established according to the transmission unit, data acquired in real time are stored in the twin data unit, and state parameters of the twin model are updated in real time;
and according to sepsis management resource control of the calling detection device, predicting pre-alarm data of the patient to be detected for sepsis according to the current detection device state parameters.
Further, the acquiring treatment archive data of the discharged rehabilitation patient, extracting rehabilitation patient detection data of the treatment archive data, and comparing the rehabilitation patient detection data with the pre-alarm data to obtain an alarm comparison result, specifically including:
acquiring discharged sepsis rehabilitation patient information, and extracting treatment archive data of the sepsis rehabilitation patient information;
performing cluster analysis of rehabilitation type on the sepsis rehabilitation patient information according to the treatment archive data to obtain a rehabilitation cluster result;
acquiring rehabilitation detection historical data of sepsis rehabilitation patient information under each rehabilitation result cluster by adopting different data extraction strategies according to the rehabilitation result clusters of the sepsis rehabilitation patient information;
and comparing the rehabilitation detection historical data of the sepsis rehabilitation patient information under the corresponding rehabilitation result cluster with the pre-alarm data to obtain an alarm comparison result.
According to a second aspect of the invention, the invention claims a digital twin based prediction system for sepsis patient association comprising:
the disease condition clustering module is used for acquiring first detection data of patients with sepsis to be detected, and clustering the disease conditions of the patients with sepsis to be detected according to the first detection data to obtain a disease condition clustering result of the patients with sepsis to be detected;
the detection module is used for acquiring second detection data of the patients to be detected under the condition aggregation set of each patient according to the condition clustering result of the patients to be detected;
the twin simulation module generates a sepsis digital twin simulation model, and the second detection data is input into the sepsis digital twin simulation model to obtain pre-alarm data of the sepsis patient to be detected;
the alarm comparison module is used for acquiring treatment archive data of the discharged rehabilitation patient, extracting rehabilitation patient detection data of the treatment archive data, and comparing the rehabilitation patient detection data with the pre-alarm data to obtain an alarm comparison result;
and the alarm output module is used for sending risk alarm prediction to different sepsis patient terminals by adopting different alarm strategies according to the alarm comparison result.
Further, the disease condition clustering module specifically includes:
acquiring first detection data of the patient to be detected with sepsis, and extracting one or more abnormality index values detected at different parts of the patient to be detected with sepsis from the first detection data;
acquiring a first abnormal index value with the largest proportion from normal values in the abnormal index values, and identifying a detection part corresponding to the first abnormal index value;
and clustering the illness states of the patients to be detected with sepsis according to the detection parts to obtain illness state clustering results of the patients to be detected with sepsis.
Further, the detection module specifically includes:
acquiring a disease clustering result of the patient to be detected with sepsis, and acquiring lymphatic tissue detection data of the patient to be detected with sepsis when the patient to be detected with sepsis belongs to a first type of potential sepsis;
when the patient to be detected is in the second type of potential sepsis, acquiring liver tissue detection data of the patient to be detected;
when the patient to be detected is in a third type of potential sepsis, acquiring kidney tissue detection data of the patient to be detected;
And when the patient to be detected is in the third type of potential sepsis, acquiring the cardiovascular and cerebrovascular tissue detection data of the patient to be detected.
Further, the twin simulation module specifically includes:
acquiring real-time operation data of the second detection data, and acquiring data comprising potential difference, detection equipment type, detection times and total detection duration according to a sensor in the detection device;
a twin correlation unit generating second detection data; generating a three-dimensional model detection data format on a U3D platform, generating a three-dimensional visual model of the detection device, and further developing data association, a digital output unit, a privacy object editing unit, a centralized management port, programmable debugging, a historical data recording unit, an expansion port and a data simulation analysis unit;
the generation resource control unit realizes corresponding resource control according to the port provided by the data association unit, and specifically comprises the following steps: unified port control resource control, programmable debug resource control, historical data record resource control, data access resource control, data cycle resource control, and detection device sepsis management resource control;
mapping among the units is established according to the transmission unit, data acquired in real time are stored in the twin data unit, and state parameters of the twin model are updated in real time;
And according to sepsis management resource control of the calling detection device, predicting pre-alarm data of the patient to be detected for sepsis according to the current detection device state parameters.
Further, the alarm comparison module specifically includes:
acquiring discharged sepsis rehabilitation patient information, and extracting treatment archive data of the sepsis rehabilitation patient information;
performing cluster analysis of rehabilitation type on the sepsis rehabilitation patient information according to the treatment archive data to obtain a rehabilitation cluster result;
acquiring rehabilitation detection historical data of sepsis rehabilitation patient information under each rehabilitation result cluster by adopting different data extraction strategies according to the rehabilitation result clusters of the sepsis rehabilitation patient information;
and comparing the rehabilitation detection historical data of the sepsis rehabilitation patient information under the corresponding rehabilitation result cluster with the pre-alarm data to obtain an alarm comparison result.
The invention discloses a prediction method and a prediction system for association of sepsis patients based on digital twinning, which are used for acquiring first detection data of sepsis patients to be detected, and clustering the illness states of the sepsis patients to be detected to obtain illness state clustering results; acquiring second detection data of patients to be detected with sepsis under the condition aggregation sets of all patients by adopting different data detection strategies; generating a sepsis digital twin simulation model, and inputting second detection data to obtain pre-alarm data of a patient to be detected sepsis; acquiring treatment archive data of the discharged rehabilitation patient, extracting detection data of the rehabilitation patient and pre-alarm data for comparison, and obtaining an alarm comparison result; and sending risk alarm predictions to different sepsis patient terminals by adopting different alarm strategies. The invention can effectively extract and analyze data according to different strategies of the damaged part of the sepsis patient, and combines the historical rehabilitation patient data to perform early warning comparison, thereby improving the correlation predictability of the sepsis.
Drawings
FIG. 1 is a workflow diagram of a method of predicting sepsis patient associations based on digital twinning as claimed in the present invention;
FIG. 2 is a second workflow diagram of a digital twin based method of prediction of sepsis patient association in accordance with the claimed subject matter;
FIG. 3 is a third workflow diagram of a digital twin based method of predicting sepsis patient association in accordance with the claimed subject matter;
fig. 4 is a block diagram of a digital twin based sepsis patient associated prediction system in accordance with the present invention.
Detailed Description
According to a first embodiment of the invention, referring to fig. 1, the invention claims a method for predicting sepsis patient association based on digital twinning, comprising:
acquiring first detection data of a patient to be detected with sepsis, and clustering the illness state of the patient to be detected with sepsis according to the first detection data to obtain an illness state clustering result of the patient to be detected with sepsis;
acquiring second detection data of the sepsis patients to be detected under the condition aggregation sets of all the patients by adopting different data detection strategies according to the condition clustering results of the sepsis patients to be detected;
Generating a sepsis digital twin simulation model, and inputting the second detection data into the sepsis digital twin simulation model to obtain pre-alarm data of the patient to be detected;
acquiring treatment archive data of the discharged rehabilitation patient, extracting rehabilitation patient detection data of the treatment archive data, and comparing the rehabilitation patient detection data with the pre-alarm data to obtain an alarm comparison result;
and according to the alarm comparison result, adopting different alarm strategies to send risk alarm predictions to different sepsis patient terminals.
Clinical research on the pathogenesis of sepsis has been advanced to a certain extent, but the pathogenesis of sepsis is complex, involving more variable factors, and the diagnosis accuracy is still to be improved. Studies have shown that early detection of sepsis and timely antibiotic treatment are critical to improving the risk of mortality in sepsis patients, with mortality increased by 4% -8% each hour of delay in treatment. Patients who are likely to develop sepsis are discovered as early as possible and timely treated, and the method has important research value and significance for improving the survival rate of the patients in the ICU.
Further, referring to fig. 2, the acquiring first detection data of the patient with sepsis to be detected, and clustering the patient with sepsis to be detected according to the first detection data, to obtain a disease clustering result of the patient with sepsis to be detected specifically includes:
acquiring first detection data of the patient to be detected with sepsis, and extracting one or more abnormality index values detected at different parts of the patient to be detected with sepsis from the first detection data;
acquiring a first abnormal index value with the largest proportion from normal values in the abnormal index values, and identifying a detection part corresponding to the first abnormal index value;
and clustering the illness states of the patients to be detected with sepsis according to the detection parts to obtain illness state clustering results of the patients to be detected with sepsis.
Sepsis is generally a relatively dangerous condition, with a high mortality rate, and approximately 9% of sepsis patients will develop septic shock and multiple organ dysfunction, and more than half of the deaths in the intensive care unit are caused by septic shock and multiple organ dysfunction, with sepsis being the leading cause of death in non-cardiac patients in the intensive care unit.
Therefore, in this embodiment, it is necessary to extract the detection site corresponding to the abnormality index value from the first detection data of the sepsis patient to be detected.
The first detection data are blood detection data, and the detection content at least comprises: hemoglobin content, serum creatinine, serum bilirubin, platelet count;
the plurality of abnormality index values are derived from detection values of hemoglobin content, serum creatinine, serum bilirubin, and platelet count;
the detection part corresponding to the first abnormal index value is cardiovascular and cerebrovascular, kidney, liver and lymphatic tissue.
When the detection value of the platelet count is a first abnormal index value with the largest proportion deviated from the normal value, identifying the detection part corresponding to the first abnormal index value as lymphoid tissue, and obtaining the condition clustering result of the patient to be detected as a first type of potential sepsis;
when the detection value of serum bilirubin is a first abnormal index value with the largest proportion deviated from a normal value, identifying a detection part corresponding to the first abnormal index value as liver tissue, and obtaining a condition clustering result of the patient to be detected as a second type of potential sepsis;
when the serum creatinine is a first abnormal index value with the largest proportion from a normal value, identifying a detection part corresponding to the first abnormal index value as kidney tissue, and obtaining a disease clustering result of the patient with sepsis to be detected as a third type of potential sepsis;
When the detection value of the hemoglobin content is a first abnormal index value with the largest proportion deviated from the normal value, identifying the detection part corresponding to the first abnormal index value as cardiovascular and cerebrovascular tissues, and obtaining the condition clustering result of the patient to be detected with sepsis as a fourth type of potential sepsis;
further, according to the disease condition clustering result of the patient to be detected with sepsis, acquiring second detection data of the patient to be detected with sepsis under each patient disease condition aggregation set by adopting different data detection strategies, specifically including:
acquiring a disease clustering result of the patient to be detected with sepsis, and acquiring lymphatic tissue detection data of the patient to be detected with sepsis when the patient to be detected with sepsis belongs to a first type of potential sepsis;
when the patient to be detected is in the second type of potential sepsis, acquiring liver tissue detection data of the patient to be detected;
when the patient to be detected is in a third type of potential sepsis, acquiring kidney tissue detection data of the patient to be detected;
and when the patient to be detected is in the fourth type of potential sepsis, acquiring the cardiovascular and cerebrovascular tissue detection data of the patient to be detected.
Wherein in this embodiment, the second detection data corresponding to the lymphoid tissue detection data comprises red blood cell count, white blood cell count, lactate dehydrogenase, and erythrocyte sedimentation rate;
the second detection data corresponding to the liver tissue detection data comprises glutamic pyruvic transaminase content, direct bilirubin content, indirect bilirubin content and glutamic pyruvic transaminase content;
the second detection data corresponding to the kidney tissue detection data comprises urea nitrogen and blood uric acid;
the second detection data corresponding to the cardiovascular and cerebrovascular tissue detection data comprises: blood pressure value, blood lipid value, total cholesterol value;
further, referring to fig. 3, the generating a sepsis digital twin simulation model, inputting the second detection data into the sepsis digital twin simulation model, to obtain pre-alarm data of the sepsis patient to be detected, specifically includes:
acquiring real-time operation data of the second detection data, and acquiring data comprising potential difference, detection equipment type, detection times and total detection duration according to a sensor in the detection device;
a twin correlation unit generating second detection data; generating a three-dimensional model detection data format on a U3D platform, generating a three-dimensional visual model of the detection device, and further developing data association, a digital output unit, a privacy object editing unit, a centralized management port, programmable debugging, a historical data recording unit, an expansion port and a data simulation analysis unit;
The generation resource control unit realizes corresponding resource control according to the port provided by the data association unit, and specifically comprises the following steps: unified port control resource control, programmable debug resource control, historical data record resource control, data access resource control, data cycle resource control, and detection device sepsis management resource control;
mapping among the units is established according to the transmission unit, data acquired in real time are stored in the twin data unit, and state parameters of the twin model are updated in real time;
and according to sepsis management resource control of the calling detection device, predicting pre-alarm data of the patient to be detected for sepsis according to the current detection device state parameters.
Wherein in this embodiment the digital twin model of the second detection data comprises a real object unit PO, a transmission unit TU, a twin correlation unit TR, a resource control unit SU, a twin data unit TWU; the mathematical description output is:
Model＝{PO,TR,TU,SU,TWU} (1)
the actual object unit is a composition foundation of the twin model and comprises a second detection data detection device, a sensor acquisition device and a standardized data communication and migration device;
the transmission unit indicates the mode in which the data is delivered to the twin association unit, and a data collector is arranged between the transmission unit and the twin association unit and used as multi-source heterogeneous data transfer; the data acquisition device acquires and processes the data of the equipment, acquires and processes multi-source heterogeneous data by using a TCP protocol at a transmission layer of data transmission, and delivers the data to the twin association unit by using HTTP\HTTPS; the Bluetooth protocol is adopted to collect multi-source heterogeneous data, and then the TCP protocol is used to deliver the data to the twin association unit.
The twin association unit also comprises a predicted future running state of the equipment according to past experience and data; the unit uses the encapsulated multi-source heterogeneous data acquired by the data acquisition unit to process and use the data related to the functions of the twin association unit.
The digital output unit comprises three-dimensional simulation and data visualization capabilities of an actual object; the user-defined loading, displaying and modifying of different three-dimensional models are realized on the U3D platform; the platform can realize the generation and loading of the model, and the generated detection data format and the integrity of the detection data are verified before the generation of the model; when the program runs, the loading of the model needs to check and process the three-dimensional detection data in various formats, and the processed detection data type comprises a three-dimensional model; and (3) loading the 3D model according to processing the detection data of different models, and obtaining the privacy object after loading.
The data association unit is completed by a data collector between the transmission unit and the simple fitting unit, and is used for cooperatively packaging the multi-source heterogeneous data and transmitting the multi-source heterogeneous data to the simple fitting node, and simultaneously, the actual object control instruction transmitted by the simple fitting node can be back-propagated.
The resource control unit realizes corresponding resource control according to the port provided by the data association unit; privacy object editing resource control enables sepsis patients to make a simple fit to privacy object assembly; unified port control resource control enables sepsis patients to control a privacy object using instructions, providing the ability to drive the privacy object out; the historical data record resource control is used for recording operation data and abnormal information generated by the frame when the program is operated; the data analysis and statistics of the production data required by the sepsis patients are displayed; the instruction sequence is for receiving sepsis patient instructions.
The twin data unit is a core driving force of the twin model, and refers to all data generated and operated in the process of the twin model and derived data obtained by performing data analysis by diagnostic resource control; storing data acquired by an actual object unit and twin data generated by a twin data unit by adopting an Oracle relational database;
the twin data comprise actual object unit PO data, twin data unit TR data, resource control unit SU data, knowledge data and fusion derivative data; the mathematical output is as follows:
TWU＝{TWp,TWv,TWs,TWk,TWf} (2)
TWp the specification, performance and actual attribute of the actual object unit and the running condition and environmental parameter reflecting the actual object;
TWv the privacy object TR related data includes geometric model related data corresponding to the specification, load and feature of the actual object and behavior condition model data related to the constraint, condition and association;
TWs represent algorithms, behavioral limitations, and data processing method related data required during system operation and data that need to be stored in a database during operation for later analysis training;
TWk includes expert knowledge, disease criteria, condition constraints, reasoning methodologies, and data related to model libraries from a common algorithm library;
TWf shows derived data obtained by preprocessing, mapping, migration, clustering, association, collaboration, and fusion of TWp, TWv, TWs, TWk.
Further, the acquiring treatment archive data of the discharged rehabilitation patient, extracting rehabilitation patient detection data of the treatment archive data, and comparing the rehabilitation patient detection data with the pre-alarm data to obtain an alarm comparison result, specifically including:
acquiring discharged sepsis rehabilitation patient information, and extracting treatment archive data of the sepsis rehabilitation patient information;
Performing cluster analysis of rehabilitation type on the sepsis rehabilitation patient information according to the treatment archive data to obtain a rehabilitation cluster result;
acquiring rehabilitation detection historical data of sepsis rehabilitation patient information under each rehabilitation result cluster by adopting different data extraction strategies according to the rehabilitation result clusters of the sepsis rehabilitation patient information;
and comparing the rehabilitation detection historical data of the sepsis rehabilitation patient information under the corresponding rehabilitation result cluster with the pre-alarm data to obtain an alarm comparison result.
In this embodiment, acquiring rehabilitation detection history data of sepsis rehabilitation patient information under each rehabilitation result cluster by using different data extraction strategies refers to acquiring rehabilitation detection history data of a corresponding part according to a detection part corresponding to the sepsis rehabilitation patient information.
Further, according to the alarm comparison result, different alarm strategies are adopted to send risk alarm predictions to different sepsis patient terminals;
the different alarm strategies refer to different sepsis patient terminals and send consultation requests to different medical departments.
According to a second embodiment of the invention, referring to fig. 4, the invention claims a digital twin based sepsis patient associated prediction system comprising:
The disease condition clustering module is used for acquiring first detection data of patients with sepsis to be detected, and clustering the disease conditions of the patients with sepsis to be detected according to the first detection data to obtain a disease condition clustering result of the patients with sepsis to be detected;
the detection module is used for acquiring second detection data of the patients to be detected under the condition aggregation set of each patient according to the condition clustering result of the patients to be detected;
the twin simulation module generates a sepsis digital twin simulation model, and the second detection data is input into the sepsis digital twin simulation model to obtain pre-alarm data of the sepsis patient to be detected;
the alarm comparison module is used for acquiring treatment archive data of the discharged rehabilitation patient, extracting rehabilitation patient detection data of the treatment archive data, and comparing the rehabilitation patient detection data with the pre-alarm data to obtain an alarm comparison result;
and the alarm output module is used for sending risk alarm prediction to different sepsis patient terminals by adopting different alarm strategies according to the alarm comparison result.
Further, the disease condition clustering module specifically includes:
Acquiring first detection data of the patient to be detected with sepsis, and extracting one or more abnormality index values detected at different parts of the patient to be detected with sepsis from the first detection data;
acquiring a first abnormal index value with the largest proportion from normal values in the abnormal index values, and identifying a detection part corresponding to the first abnormal index value;
and clustering the illness states of the patients to be detected with sepsis according to the detection parts to obtain illness state clustering results of the patients to be detected with sepsis.
Further, the detection module specifically includes:
acquiring a disease clustering result of the patient to be detected with sepsis, and acquiring lymphatic tissue detection data of the patient to be detected with sepsis when the patient to be detected with sepsis belongs to a first type of potential sepsis;
when the patient to be detected is in the second type of potential sepsis, acquiring liver tissue detection data of the patient to be detected;
when the patient to be detected is in a third type of potential sepsis, acquiring kidney tissue detection data of the patient to be detected;
and when the patient to be detected is in the third type of potential sepsis, acquiring the cardiovascular and cerebrovascular tissue detection data of the patient to be detected.
Further, the twin simulation module specifically includes:
acquiring real-time operation data of the second detection data, and acquiring data comprising potential difference, detection equipment type, detection times and total detection duration according to a sensor in the detection device;
a twin correlation unit generating second detection data; generating a three-dimensional model detection data format on a U3D platform, generating a three-dimensional visual model of the detection device, and further developing data association, a digital output unit, a privacy object editing unit, a centralized management port, programmable debugging, a historical data recording unit, an expansion port and a data simulation analysis unit;
the generation resource control unit realizes corresponding resource control according to the port provided by the data association unit, and specifically comprises the following steps: unified port control resource control, programmable debug resource control, historical data record resource control, data access resource control, data cycle resource control, and detection device sepsis management resource control;
mapping among the units is established according to the transmission unit, data acquired in real time are stored in the twin data unit, and state parameters of the twin model are updated in real time;
and according to sepsis management resource control of the calling detection device, predicting pre-alarm data of the patient to be detected for sepsis according to the current detection device state parameters.
Further, the alarm comparison module specifically includes:
acquiring discharged sepsis rehabilitation patient information, and extracting treatment archive data of the sepsis rehabilitation patient information;
performing cluster analysis of rehabilitation type on the sepsis rehabilitation patient information according to the treatment archive data to obtain a rehabilitation cluster result;
acquiring rehabilitation detection historical data of sepsis rehabilitation patient information under each rehabilitation result cluster by adopting different data extraction strategies according to the rehabilitation result clusters of the sepsis rehabilitation patient information;
and comparing the rehabilitation detection historical data of the sepsis rehabilitation patient information under the corresponding rehabilitation result cluster with the pre-alarm data to obtain an alarm comparison result.
Those skilled in the art will appreciate that various modifications and improvements can be made to the disclosure. For example, the various detection devices or components described above may be implemented in hardware, software, firmware, or a combination of some or all of the three.
A flowchart is used in this disclosure to describe the steps of a method according to an embodiment of the present disclosure. It should be understood that the steps that follow or before do not have to be performed in exact order. Rather, the various steps may be processed in reverse order or simultaneously. Also, other operations may be added to these processes.
It will be appreciated by those of ordinary skill in the art that all or part of the steps of the methods described above may be performed by associated hardware, and that the program may be stored on a computer readable storage medium, such as a read only memory, a magnetic or optical disk, or the like. Alternatively, all or part of the steps of the above embodiments may be implemented using one or more synergistic circuits. Accordingly, each unit/unit in the above embodiments may be implemented in the form of hardware, or may be implemented in the form of a software functional unit. The present disclosure is not limited to any specific form of combination of hardware and software.
Unless defined otherwise, all terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The foregoing is illustrative of the present disclosure and is not to be construed as limiting thereof. Although a few exemplary embodiments of this disclosure have been described, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this disclosure. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the claims. It is to be understood that the foregoing is illustrative of the present disclosure and is not to be construed as limited to the specific embodiments disclosed, and that modifications to the disclosed embodiments, as well as other embodiments, are intended to be included within the scope of the appended claims. The disclosure is defined by the claims and their equivalents.
In the description of the present specification, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (10)
1. A method of predicting sepsis patient association based on digital twinning, comprising:
acquiring first detection data of a patient to be detected with sepsis, and clustering the illness state of the patient to be detected with sepsis according to the first detection data to obtain an illness state clustering result of the patient to be detected with sepsis;
Acquiring second detection data of the sepsis patients to be detected under the condition aggregation sets of all the patients by adopting different data detection strategies according to the condition clustering results of the sepsis patients to be detected;
generating a sepsis digital twin simulation model, and inputting the second detection data into the sepsis digital twin simulation model to obtain pre-alarm data of the patient to be detected;
acquiring treatment archive data of the discharged rehabilitation patient, extracting rehabilitation patient detection data of the treatment archive data, and comparing the rehabilitation patient detection data with the pre-alarm data to obtain an alarm comparison result;
and according to the alarm comparison result, adopting different alarm strategies to send risk alarm predictions to different sepsis patient terminals.
2. A method of predicting sepsis patient association based on digital twinning according to claim 1,
the method comprises the steps of obtaining first detection data of a patient to be detected with sepsis, clustering the illness state of the patient to be detected with sepsis according to the first detection data to obtain an illness state clustering result of the patient to be detected with sepsis, and specifically comprises the following steps:
acquiring first detection data of the patient to be detected with sepsis, and extracting one or more abnormality index values detected at different parts of the patient to be detected with sepsis from the first detection data;
Acquiring a first abnormal index value with the largest proportion from normal values in the abnormal index values, and identifying a detection part corresponding to the first abnormal index value;
and clustering the illness states of the patients to be detected with sepsis according to the detection parts to obtain illness state clustering results of the patients to be detected with sepsis.
3. A method of predicting sepsis patient association based on digital twinning according to claim 2,
according to the disease clustering result of the patient to be detected with sepsis, acquiring second detection data of the patient to be detected with sepsis under the disease aggregation set of each patient by adopting different data detection strategies, wherein the method specifically comprises the following steps:
acquiring a disease clustering result of the patient to be detected with sepsis, and acquiring lymphatic tissue detection data of the patient to be detected with sepsis when the patient to be detected with sepsis belongs to a first type of potential sepsis;
when the patient to be detected is in the second type of potential sepsis, acquiring liver tissue detection data of the patient to be detected;
when the patient to be detected is in a third type of potential sepsis, acquiring kidney tissue detection data of the patient to be detected;
And when the patient to be detected is in the third type of potential sepsis, acquiring the cardiovascular and cerebrovascular tissue detection data of the patient to be detected.
4. A method of predicting sepsis patient association based on digital twinning according to claim 3,
the step of generating a sepsis digital twin simulation model, inputting the second detection data into the sepsis digital twin simulation model to obtain pre-alarm data of the patient to be detected, specifically including:
acquiring real-time operation data of the second detection data, and acquiring data comprising potential difference, detection equipment type, detection times and total detection duration according to a sensor in the detection device;
a twin correlation unit generating second detection data; generating a three-dimensional model detection data format on a U3D platform, generating a three-dimensional visual model of the detection device, and further developing data association, a digital output unit, a privacy object editing unit, a centralized management port, programmable debugging, a historical data recording unit, an expansion port and a data simulation analysis unit;
the generation resource control unit realizes corresponding resource control according to the port provided by the data association unit, and specifically comprises the following steps: unified port control resource control, programmable debug resource control, historical data record resource control, data access resource control, data cycle resource control, and detection device sepsis management resource control;
Mapping among the units is established according to the transmission unit, data acquired in real time are stored in the twin data unit, and state parameters of the twin model are updated in real time;
and according to sepsis management resource control of the calling detection device, predicting pre-alarm data of the patient to be detected for sepsis according to the current detection device state parameters.
5. A method of predicting sepsis patient association based on digital twinning according to claim 4,
the method comprises the steps of obtaining treatment archive data of a discharged rehabilitation patient, extracting rehabilitation patient detection data of the treatment archive data, comparing the rehabilitation patient detection data with the pre-alarm data to obtain an alarm comparison result, and specifically comprises the following steps:
acquiring discharged sepsis rehabilitation patient information, and extracting treatment archive data of the sepsis rehabilitation patient information;
performing cluster analysis of rehabilitation type on the sepsis rehabilitation patient information according to the treatment archive data to obtain a rehabilitation cluster result;
acquiring rehabilitation detection historical data of sepsis rehabilitation patient information under each rehabilitation result cluster by adopting different data extraction strategies according to the rehabilitation result clusters of the sepsis rehabilitation patient information;
And comparing the rehabilitation detection historical data of the sepsis rehabilitation patient information under the corresponding rehabilitation result cluster with the pre-alarm data to obtain an alarm comparison result.
6. A digital twin based sepsis patient association prediction system comprising:
the disease condition clustering module is used for acquiring first detection data of patients with sepsis to be detected, and clustering the disease conditions of the patients with sepsis to be detected according to the first detection data to obtain a disease condition clustering result of the patients with sepsis to be detected;
the detection module is used for acquiring second detection data of the patients to be detected under the condition aggregation set of each patient according to the condition clustering result of the patients to be detected;
the twin simulation module generates a sepsis digital twin simulation model, and the second detection data is input into the sepsis digital twin simulation model to obtain pre-alarm data of the sepsis patient to be detected;
the alarm comparison module is used for acquiring treatment archive data of the discharged rehabilitation patient, extracting rehabilitation patient detection data of the treatment archive data, and comparing the rehabilitation patient detection data with the pre-alarm data to obtain an alarm comparison result;
And the alarm output module is used for sending risk alarm prediction to different sepsis patient terminals by adopting different alarm strategies according to the alarm comparison result.
7. A digital twin sepsis patient association based prediction system according to claim 6,
the illness state clustering module specifically comprises:
acquiring first detection data of the patient to be detected with sepsis, and extracting one or more abnormality index values detected at different parts of the patient to be detected with sepsis from the first detection data;
acquiring a first abnormal index value with the largest proportion from normal values in the abnormal index values, and identifying a detection part corresponding to the first abnormal index value;
and clustering the illness states of the patients to be detected with sepsis according to the detection parts to obtain illness state clustering results of the patients to be detected with sepsis.
8. A digital twin sepsis patient association based prediction system according to claim 7,
the detection module specifically comprises:
acquiring a disease clustering result of the patient to be detected with sepsis, and acquiring lymphatic tissue detection data of the patient to be detected with sepsis when the patient to be detected with sepsis belongs to a first type of potential sepsis;
When the patient to be detected is in the second type of potential sepsis, acquiring liver tissue detection data of the patient to be detected;
when the patient to be detected is in a third type of potential sepsis, acquiring kidney tissue detection data of the patient to be detected;
and when the patient to be detected is in the third type of potential sepsis, acquiring the cardiovascular and cerebrovascular tissue detection data of the patient to be detected.
9. A digital twin sepsis patient association based prediction system according to claim 8,
the twin simulation module specifically comprises:
acquiring real-time operation data of the second detection data, and acquiring data comprising potential difference, detection equipment type, detection times and total detection duration according to a sensor in the detection device;
a twin correlation unit generating second detection data; generating a three-dimensional model detection data format on a U3D platform, generating a three-dimensional visual model of the detection device, and further developing data association, a digital output unit, a privacy object editing unit, a centralized management port, programmable debugging, a historical data recording unit, an expansion port and a data simulation analysis unit;
The generation resource control unit realizes corresponding resource control according to the port provided by the data association unit, and specifically comprises the following steps: unified port control resource control, programmable debug resource control, historical data record resource control, data access resource control, data cycle resource control, and detection device sepsis management resource control;
mapping among the units is established according to the transmission unit, data acquired in real time are stored in the twin data unit, and state parameters of the twin model are updated in real time;
and according to sepsis management resource control of the calling detection device, predicting pre-alarm data of the patient to be detected for sepsis according to the current detection device state parameters.
10. A digital twin sepsis patient association based prediction system according to claim 9,
the alarm comparison module specifically comprises:
acquiring discharged sepsis rehabilitation patient information, and extracting treatment archive data of the sepsis rehabilitation patient information;
performing cluster analysis of rehabilitation type on the sepsis rehabilitation patient information according to the treatment archive data to obtain a rehabilitation cluster result;
acquiring rehabilitation detection historical data of sepsis rehabilitation patient information under each rehabilitation result cluster by adopting different data extraction strategies according to the rehabilitation result clusters of the sepsis rehabilitation patient information;
And comparing the rehabilitation detection historical data of the sepsis rehabilitation patient information under the corresponding rehabilitation result cluster with the pre-alarm data to obtain an alarm comparison result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311048018.4A CN116805520B (en) | 2023-08-21 | 2023-08-21 | Digital twinning-based sepsis patient association prediction method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311048018.4A CN116805520B (en) | 2023-08-21 | 2023-08-21 | Digital twinning-based sepsis patient association prediction method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116805520A true CN116805520A (en) | 2023-09-26 |
| CN116805520B CN116805520B (en) | 2023-11-10 |
Family
ID=88080831
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311048018.4A Active CN116805520B (en) | 2023-08-21 | 2023-08-21 | Digital twinning-based sepsis patient association prediction method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116805520B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117789965A (en) * | 2024-02-23 | 2024-03-29 | 北京惠每云科技有限公司 | Venous thrombosis prevention simulation evaluation method and system based on digital twin |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210241909A1 (en) * | 2020-02-03 | 2021-08-05 | Koninklijke Philips N.V. | Method and a system for evaluating treatment strategies on a virtual model of a patient |
| WO2022216220A1 (en) * | 2021-04-07 | 2022-10-13 | Biosigns Pte. Ltd. | Method and system for personalized prediction of infection and sepsis |
| CN115206551A (en) * | 2022-08-10 | 2022-10-18 | 北京京东拓先科技有限公司 | Health state monitoring method and device based on digital twins |
| WO2022225460A1 (en) * | 2021-04-20 | 2022-10-27 | Mesh Bio Pte. Ltd. | Method and system for generating a metabolic digital twin for clinical decision support |
| US20230070665A1 (en) * | 2021-09-09 | 2023-03-09 | GenoEmote LLC | Method and system for validation of disease condition reprogramming based on personality to disease condition mapping |
| CN116110597A (en) * | 2023-01-30 | 2023-05-12 | 深圳市平行维度科技有限公司 | Digital twinning-based intelligent analysis method and device for patient disease categories |
| CN116597981A (en) * | 2023-05-10 | 2023-08-15 | 复旦大学附属中山医院 | Method for preventing, controlling and managing chronic obstructive pulmonary disease by combining digital twin technology |
-
2023
- 2023-08-21 CN CN202311048018.4A patent/CN116805520B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210241909A1 (en) * | 2020-02-03 | 2021-08-05 | Koninklijke Philips N.V. | Method and a system for evaluating treatment strategies on a virtual model of a patient |
| WO2022216220A1 (en) * | 2021-04-07 | 2022-10-13 | Biosigns Pte. Ltd. | Method and system for personalized prediction of infection and sepsis |
| WO2022225460A1 (en) * | 2021-04-20 | 2022-10-27 | Mesh Bio Pte. Ltd. | Method and system for generating a metabolic digital twin for clinical decision support |
| US20230070665A1 (en) * | 2021-09-09 | 2023-03-09 | GenoEmote LLC | Method and system for validation of disease condition reprogramming based on personality to disease condition mapping |
| CN115206551A (en) * | 2022-08-10 | 2022-10-18 | 北京京东拓先科技有限公司 | Health state monitoring method and device based on digital twins |
| CN116110597A (en) * | 2023-01-30 | 2023-05-12 | 深圳市平行维度科技有限公司 | Digital twinning-based intelligent analysis method and device for patient disease categories |
| CN116597981A (en) * | 2023-05-10 | 2023-08-15 | 复旦大学附属中山医院 | Method for preventing, controlling and managing chronic obstructive pulmonary disease by combining digital twin technology |
Non-Patent Citations (3)
| Title |
|---|
| LAL AMOS 等: "Development and Verification of a Digital Twin Patient Model to Predict Specific Treatment Response During the First 24 Hours of Sepsis", 《 CRITICAL CARE EXPLORATIONS》, vol. 2, no. 11, pages 249 * |
| 周晓丰 等: "炎性标记物与脓毒症患者病情严重程度量化关系", 《临床荟萃》, vol. 31, no. 11, pages 1209 - 1212 * |
| 范晨皓 等: "基于大数据的全程健康管理服务模式探讨", 《中国医学装备》, vol. 15, no. 03, pages 130 - 134 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117789965A (en) * | 2024-02-23 | 2024-03-29 | 北京惠每云科技有限公司 | Venous thrombosis prevention simulation evaluation method and system based on digital twin |
| CN117789965B (en) * | 2024-02-23 | 2024-06-07 | 北京惠每云科技有限公司 | Venous thrombosis prevention simulation evaluation method and system based on digital twin |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116805520B (en) | 2023-11-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11468998B2 (en) | Methods and systems for software clinical guidance | |
| Bichindaritz et al. | Case-based reasoning in the health sciences: What's next? | |
| Ismail et al. | Healthcare analysis in smart big data analytics: reviews, challenges and recommendations | |
| CN116805520B (en) | Digital twinning-based sepsis patient association prediction method and system | |
| CN110880362A (en) | Large-scale medical data knowledge mining and treatment scheme recommending system | |
| CN113270192A (en) | Hemodialysis risk prediction system based on combination of dynamic and static data and depth self-encoder | |
| US20220310260A1 (en) | System and Methods for Knowledge Representation and Reasoning in Clinical Procedures | |
| CN113990502B (en) | ICU heart failure prediction system based on heterogeneous graph neural network | |
| CN116259396A (en) | Treatment expense prediction method, system, equipment and storage medium based on machine learning | |
| CN112786190B (en) | Medical health diagnosis and treatment method based on multidimensional data fusion | |
| CN114611879A (en) | Clinical risk prediction system based on multitask learning | |
| Subasi et al. | Digital twins in healthcare and biomedicine | |
| Peixoto et al. | Predicting resurgery in intensive care-a data mining approach | |
| CN116230224A (en) | Method and system for predicting adverse events of heart failure based on time sequence model | |
| US11756200B2 (en) | Medical imaging with functional architecture tracking | |
| Zhu et al. | Is larger always better? Evaluating and prompting large language models for non-generative medical tasks | |
| Lamb et al. | Artificial intelligence in medicine and male infertility | |
| CN114255878A (en) | Training method, system, device and storage medium of disease typing model | |
| Cypko et al. | Development of a Clinical Decision Support System | |
| CN114098638A (en) | Interpretable dynamic disease severity prediction method | |
| Bala et al. | Applications of Machine Learning and Deep Learning for maintaining Electronic Health Records | |
| Gaebel et al. | Modular architecture for integrated model-based decision support | |
| Singh et al. | Deep Learning Models with SDN and IoT for Intelligent Healthcare | |
| Kashyap et al. | Can semantic web technologies enable translational medicine? | |
| CN115295147A (en) | Renal failure AI doctor system and renal failure medical system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |