CN111785370B - Medical record data processing method and device, computer storage medium and electronic equipment - Google Patents
Medical record data processing method and device, computer storage medium and electronic equipment Download PDFInfo
- Publication number
- CN111785370B CN111785370B CN202010632725.8A CN202010632725A CN111785370B CN 111785370 B CN111785370 B CN 111785370B CN 202010632725 A CN202010632725 A CN 202010632725A CN 111785370 B CN111785370 B CN 111785370B
- Authority
- CN
- China
- Prior art keywords
- disease
- target
- medical
- sequence
- data set
- 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.)
- Active
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 27
- 201000010099 disease Diseases 0.000 claims abstract description 248
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 248
- 238000012545 processing Methods 0.000 claims abstract description 42
- 238000005065 mining Methods 0.000 claims abstract description 32
- 238000000034 method Methods 0.000 claims description 34
- 208000017667 Chronic Disease Diseases 0.000 claims description 13
- 238000003745 diagnosis Methods 0.000 claims description 13
- 230000003211 malignant effect Effects 0.000 claims description 13
- 230000001575 pathological effect Effects 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 2
- 238000011161 development Methods 0.000 abstract description 16
- 238000011282 treatment Methods 0.000 abstract description 7
- 238000005516 engineering process Methods 0.000 abstract description 6
- 238000011160 research Methods 0.000 abstract description 6
- 206010020772 Hypertension Diseases 0.000 description 21
- 208000005577 Gastroenteritis Diseases 0.000 description 16
- 208000005718 Stomach Neoplasms Diseases 0.000 description 16
- 206010017758 gastric cancer Diseases 0.000 description 16
- 201000011549 stomach cancer Diseases 0.000 description 16
- 208000031226 Hyperlipidaemia Diseases 0.000 description 11
- 230000008569 process Effects 0.000 description 10
- 201000006549 dyspepsia Diseases 0.000 description 9
- 206010006784 Burning sensation Diseases 0.000 description 5
- 208000013738 Sleep Initiation and Maintenance disease Diseases 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 208000002173 dizziness Diseases 0.000 description 5
- 206010022437 insomnia Diseases 0.000 description 5
- 229940079593 drug Drugs 0.000 description 4
- 239000003814 drug Substances 0.000 description 4
- 210000002784 stomach Anatomy 0.000 description 4
- 230000007774 longterm Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000002265 prevention Effects 0.000 description 3
- 230000003449 preventive effect Effects 0.000 description 3
- 238000001356 surgical procedure Methods 0.000 description 3
- 206010006895 Cachexia Diseases 0.000 description 2
- 206010010741 Conjunctivitis Diseases 0.000 description 2
- 208000006083 Hypokinesia Diseases 0.000 description 2
- 206010028813 Nausea Diseases 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 230000001684 chronic effect Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 208000026500 emaciation Diseases 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 206010067484 Adverse reaction Diseases 0.000 description 1
- 208000002177 Cataract Diseases 0.000 description 1
- JZUFKLXOESDKRF-UHFFFAOYSA-N Chlorothiazide Chemical compound C1=C(Cl)C(S(=O)(=O)N)=CC2=C1NCNS2(=O)=O JZUFKLXOESDKRF-UHFFFAOYSA-N 0.000 description 1
- 206010058467 Lung neoplasm malignant Diseases 0.000 description 1
- 206010028817 Nausea and vomiting symptoms Diseases 0.000 description 1
- 206010047700 Vomiting Diseases 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000006838 adverse reaction Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000001363 autoimmune Effects 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 208000029078 coronary artery disease Diseases 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 206010012601 diabetes mellitus Diseases 0.000 description 1
- 235000006694 eating habits Nutrition 0.000 description 1
- 230000008451 emotion Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 229960002003 hydrochlorothiazide Drugs 0.000 description 1
- NDDAHWYSQHTHNT-UHFFFAOYSA-N indapamide Chemical compound CC1CC2=CC=CC=C2N1NC(=O)C1=CC=C(Cl)C(S(N)(=O)=O)=C1 NDDAHWYSQHTHNT-UHFFFAOYSA-N 0.000 description 1
- 229960004569 indapamide Drugs 0.000 description 1
- 201000005202 lung cancer Diseases 0.000 description 1
- 208000020816 lung neoplasm Diseases 0.000 description 1
- 238000002483 medication Methods 0.000 description 1
- 238000011369 optimal treatment Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000002601 radiography Methods 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 229940126585 therapeutic drug Drugs 0.000 description 1
- 210000002700 urine Anatomy 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
- 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
-
- 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
- G16H70/00—ICT specially adapted for the handling or processing of medical references
- G16H70/20—ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Epidemiology (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Bioethics (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The disclosure relates to the technical field of data processing, and provides a medical record data processing method, a medical record data processing device, a computer storage medium and electronic equipment, wherein the medical record data processing method comprises the following steps: classifying medical record data containing target diseases to obtain an integrated data set; step A: determining a target data set from the integrated data set according to the type of the target disease; and (B) step (B): and (3) mining the sequence mode of the target data set to obtain a predicted disease sequence corresponding to the target disease. The medical record data processing method can quickly mine the development and evolution rules of specific diseases based on medical record data, solves the technical problems of lower accuracy and smaller coverage caused by predicting the evolution trend of the patient's illness state only by the personal experience of a clinician in the related technology, improves the comprehensiveness of disease state prediction, and provides effective reference for medical scientific research and clinical treatment.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a medical record data processing method, a medical record data processing device, a computer storage medium, and an electronic apparatus.
Background
With the development of social economy, the life style and life habit of people are changed, the disease spectrum is changed greatly, a plurality of novel diseases appear, the life and health of people are influenced greatly, and part of diseases with extremely strong infectivity even have no emotion to take away the lives of people. Thus, how to effectively predict the evolution trend of a patient's condition is a focus of attention of the relevant technicians.
In traditional approaches, the evolution trend of a patient's condition is generally predicted based on the personal experience of the clinician and the medical knowledge accumulated by the clinician. However, experience is a long-term accumulated process, and there is some subjectivity, and its coverage is limited and the prediction results are not comprehensive enough.
In view of this, there is a need in the art to develop a new medical record data processing method and apparatus.
It should be noted that the information disclosed in the foregoing background section is only for enhancing understanding of the background of the present disclosure.
Disclosure of Invention
The present disclosure is directed to a medical record data processing method, a medical record data processing device, a computer storage medium and an electronic device, and further, at least to a certain extent, to avoid the defect that the patient's condition evolution trend cannot be comprehensively predicted in the related art.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a medical record data processing method, including: classifying medical record data containing target diseases to obtain an integrated data set; step A: determining a target data set from the integrated data set according to the type of the target disease; and (B) step (B): and performing sequence pattern mining on the target data set to obtain a predicted disease sequence corresponding to the target disease.
In an exemplary embodiment of the present disclosure, after obtaining the predicted disease sequence corresponding to the target disease, the method further comprises: judging whether a specified disease exists in the predicted disease sequence based on a medical knowledge base, wherein the specified disease is a disease which does not have a pathological association relation with the target disease; if so, rejecting the specified disease from the integrated dataset; and (3) repeatedly executing the step A and the step B to obtain a new predicted disease sequence corresponding to the target disease.
In an exemplary embodiment of the present disclosure, the integrated dataset includes a plurality of sequences of medical events corresponding to a plurality of patients; said determining a target dataset from said integrated dataset according to a type of said target disease, comprising: according to the type of the target disease, selecting a characteristic subsequence from each medical event sequence; the target data set is determined from a plurality of the feature subsequences.
In exemplary embodiments of the present disclosure, the type of the target disease includes chronic diseases and malignant diseases; the selecting a characteristic subsequence from each medical event sequence according to the type of the target disease, includes: when the type of the target disease is chronic disease, selecting a sequence after diagnosis of the target disease from each of the medical event sequences as the characteristic subsequence; and when the type of the target disease is malignant disease, selecting a sequence before diagnosis of the target disease from the medical event sequences as the characteristic subsequence.
In an exemplary embodiment of the present disclosure, the sequence pattern mining of the target data set to obtain a predicted disease sequence corresponding to the target disease includes: performing sequence pattern mining on the target data set based on a sequence pattern mining algorithm to obtain a plurality of candidate predicted disease sequences corresponding to the target disease; and determining the candidate predicted disease sequence with the occurrence frequency greater than a frequency threshold as the predicted disease sequence corresponding to the target disease.
In an exemplary embodiment of the present disclosure, before classifying medical record data including a target disease to obtain an integrated dataset, the method further comprises: acquiring a plurality of medical events in original medical record data corresponding to each patient, and determining a time stamp corresponding to each medical event; sorting the plurality of medical events according to the sequence from the early to the late of the time stamp to obtain a medical event sequence; and determining the medical event sequence as the medical record data.
In an exemplary embodiment of the present disclosure, after deriving the sequence of medical events, the method further comprises: determining an invalid medical event of the plurality of target medical events when the same plurality of target medical events exist in the medical event sequence; the invalid medical event is the target medical event occurring for the nth time; performing rejection processing on the invalid medical event; n is a positive integer greater than 1.
According to a second aspect of the present disclosure, there is provided a medical record data processing apparatus, comprising: the classifying processing module is used for classifying the medical record data containing the target diseases to obtain an integrated data set; a determining module for determining a target data set from the integrated data set according to the type of the target disease; and the sequence pattern mining module is used for mining the sequence pattern of the target data set to obtain a predicted disease sequence corresponding to the target disease.
According to a third aspect of the present disclosure, there is provided a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the medical record data processing method according to the first aspect.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the medical record data processing method of the first aspect described above via execution of the executable instructions.
As can be seen from the above technical solutions, the medical record data processing method, the medical record data processing device, the computer storage medium and the electronic device in the exemplary embodiments of the present disclosure have at least the following advantages and positive effects:
in the technical schemes provided by some embodiments of the present disclosure, on one hand, medical record data including a target disease is classified to obtain an integrated data set, and the target data set is determined from the integrated data set according to the type of the target disease, so that redundant data which does not conform to the disease development condition can be removed for different disease types, and the target data set which conforms to the disease development condition is reserved, thereby ensuring the subsequent data processing speed and reliability. On the other hand, the sequence pattern mining is carried out on the target data set to obtain a predicted disease sequence corresponding to the target disease, the development and evolution rule of the specific disease can be rapidly mined, the technical problems of low accuracy and small coverage caused by predicting the evolution trend of the patient disease by only using the personal experience of a clinician in the related technology are solved, the prediction comprehensiveness is improved, a prevention reference is provided for part of early patients, and an effective reference is provided for medical scientific research and clinical treatment.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 is a flow chart of a medical record data processing method according to an exemplary embodiment of the present disclosure;
FIG. 2 is a schematic view showing a sub-flowchart of a medical record data processing method according to an exemplary embodiment of the present disclosure;
FIG. 3 is a schematic view showing a sub-flowchart of a medical record data processing method according to an exemplary embodiment of the present disclosure;
FIG. 4 is a flow chart illustrating an overall process of a medical record data processing method according to an exemplary embodiment of the present disclosure;
FIG. 5 is a schematic diagram of a medical record data processing device according to an exemplary embodiment of the present disclosure;
FIG. 6 illustrates a schematic diagram of a computer storage medium in an exemplary embodiment of the present disclosure;
fig. 7 shows a schematic structural diagram of an electronic device in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
The terms "a," "an," "the," and "said" are used in this specification to denote the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.; the terms "first" and "second" and the like are used merely as labels, and are not intended to limit the number of their objects.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
In traditional approaches, the evolution trend of a patient's condition is generally predicted based on the personal experience of the clinician and the medical knowledge accumulated by the clinician. However, experience is a long-term accumulation process and there is some subjectivity. Moreover, the coverage is limited, and the prediction result is not comprehensive enough.
In addition, for some difficult and complicated cases with lower morbidity and more complex etiology, the evolution trend is still unknown, and if the patient is only based on the personal experience of the clinician, it is difficult for the patient to learn the trend of the own condition.
In an embodiment of the present disclosure, a medical record data processing method is provided first, which at least overcomes the defect that the related art cannot comprehensively predict the evolution trend of the patient.
Fig. 1 is a flow chart illustrating a medical record data processing method according to an exemplary embodiment of the present disclosure, and an execution subject of the medical record data processing method may be a server for processing medical record data.
Referring to fig. 1, a medical record data processing method according to one embodiment of the present disclosure includes the steps of:
step S110, classifying and processing the medical record data containing the target diseases to obtain an integrated data set;
Step S120 (step A), determining a target data set from the integrated data set according to the type of the target disease;
Step S130 (step B), sequence pattern mining is carried out on the target data set, and a predicted disease sequence corresponding to the target disease is obtained.
In the technical scheme provided by the embodiment shown in fig. 1, on one hand, medical record data including a target disease is classified to obtain an integrated data set, and the target data set is determined from the integrated data set according to the type of the target disease, so that redundant data which does not conform to the disease development condition can be removed for different disease types, and the target data set which conforms to the disease development condition is reserved, thereby ensuring the subsequent data processing speed and reliability. On the other hand, the sequence pattern mining is carried out on the target data set to obtain a predicted disease sequence corresponding to the target disease, the development and evolution rule of the specific disease can be rapidly mined, the technical problems of low accuracy and small coverage caused by predicting the evolution trend of the patient disease by only using the personal experience of a clinician in the related technology are solved, the prediction comprehensiveness is improved, a prevention reference is provided for part of early patients, and an effective reference is provided for medical scientific research and clinical treatment.
The specific implementation of each step in fig. 1 is described in detail below:
In an exemplary embodiment of the present disclosure, medical record data for a plurality of patients may be acquired first. For example, referring to fig. 2, fig. 2 shows a schematic sub-flowchart of a medical data processing method according to an exemplary embodiment of the present disclosure, specifically, a schematic sub-flowchart of processing original medical record data to obtain medical record data, including steps S201-S203, and a specific embodiment is explained below in connection with fig. 2.
In step S201, a plurality of medical events in the original medical record data corresponding to each patient are acquired, and a time stamp corresponding to each medical event is determined.
The original medical record data may be electronic medical record data of a patient (a plurality of patients) with a history visit from a hospital database, the electronic medical record is a digitized medical record stored, managed, transmitted and reproduced by electronic equipment, and is used for replacing medical record information of a handwritten paper medical record, and the original medical record data mainly comprises an original record of the whole process of diagnosis and treatment of the hospital, for example, the original medical record data may include: diagnostic information (e.g., dizziness, insomnia, hypertension, gastroenteritis, hyperlipidemia, etc.), medication information (e.g., hydrochlorothiazide, indapamide, etc.), examination information (e.g., electrocardiogram, chest radiography, urine convention, etc.), surgical information (e.g., cataract surgery, heart stent surgery, heart bypass surgery, etc.), and cost information.
Wherein the medical event may be a patient diagnosed with a disease or an adverse reaction to the patient. For example, for patient a, its corresponding plurality of medical events may be: diagnosing hypertension, hyperlipidemia and gastroenteritis. Further, by way of example, it may be determined that the medical event "dizziness and insomnia" corresponds to month 2014, the medical event "diagnosis of hypertension" corresponds to month 2015, the medical event "diagnosis of hyperlipidemia" corresponds to month 2016, and the medical event "diagnosis of gastroenteritis" corresponds to month 2017.
For patient B, its corresponding plurality of medical events may be: dyspepsia, burning sensation in stomach area, advanced gastric cancer, emaciation. Further, by way of example, it may be determined that the medical event "dyspepsia" corresponds to time stamp 2014, 6 months, the medical event "burning sensation in the stomach" corresponds to time stamp 2015, 6 months, the medical event "gastric cancer advanced" corresponds to time stamp 2016, 6 months, and the medical event "emaciation" corresponds to time stamp 2017, 6 months.
In step S202, a plurality of medical events are sorted according to the order of the time stamps from the early to the late, and a medical event sequence is obtained.
After determining a plurality of medical events in the original medical record data corresponding to each patient and determining a time stamp corresponding to each medical event, the plurality of medical events can be sequenced according to the data from the early to the late of the time stamp to obtain a medical event sequence.
For example, referring to the explanation associated with step S201 above, the sequence of medical events corresponding to patient a may be: dizziness insomnia-hypertension-hyperlipidemia-gastroenteritis; the sequence of medical events corresponding to patient B may be: dyspepsia-burning sensation in the stomach area-advanced gastric cancer-wasting.
When there are a plurality of identical target medical events in the medical event sequence, invalid medical events (the invalid medical event is the target medical event occurring the nth time (N is a positive integer greater than 1)) among the plurality of target medical events may be determined, and the invalid medical events may be subjected to the rejection process. For example, when the obtained medical event sequence is "dizziness insomnia-hypertension-hyperlipidemia-gastroenteritis-nausea and hypodynamia-gastroenteritis", the target medical event "gastroenteritis" appearing 2 nd time is determined to be an ineffective medical event, and then the ineffective medical event "gastroenteritis" appearing 2 nd time can be subjected to rejection processing to process the medical event sequence as "dizziness and insomnia-hypertension-hyperlipidemia-gastroenteritis-nausea and hypodynamia". Therefore, medical events of important time nodes can be reserved, and interference of invalid data to subsequent processing processes is avoided.
In step S203, the medical event sequence is determined as medical record data.
Further, a plurality of medical event sequences corresponding to a plurality of patients may be determined as medical record data.
With continued reference to fig. 1, in step S110, medical record data including a target disease is categorized to obtain an integrated dataset.
After obtaining the medical record data, the medical record data including the target disease may be categorized to obtain an integrated dataset. The target diseases can be diseases with undefined disease development trend, diseases with higher morbidity, diseases with lower cure rate, diseases with lower morbidity but higher mortality rate and the like, can be set according to actual conditions, and belong to the protection scope of the present disclosure.
For example, when the target disease is hypertension, a plurality of pieces of medical record data including hypertension may be screened, and the plurality of pieces of medical record data may be integrated to obtain an integrated data set S G. When the target disease is advanced gastric cancer, a plurality of pieces of medical record data containing advanced gastric cancer can be screened out, and the pieces of medical record data are integrated to obtain another integrated data set S W.
In step S120 (step a), a target data set is determined from the integrated data set according to the type of the target disease.
After the integrated data set is obtained, the target data set may be determined from the integrated data set according to the type of the target disease. Types of diseases of interest may include chronic diseases and malignant diseases, among others. Chronic diseases, that is, those parts of the disease that are long-term and not easily cured, are difficult to cure, even if accompanied by life, once suffering from chronic diseases, and may require life-long medications, such as: hypertension, diabetes, coronary heart disease, etc. Malignant diseases, i.e. diseases that, once diagnosed, the life of the patient is at risk and can lead to death of the patient in a short time, for example: malignant tumor, advanced lung cancer, advanced gastric cancer, etc. Therefore, redundant data which do not accord with disease development conditions can be removed aiming at different disease types, and a target data set which accords with the disease development conditions is reserved, so that the subsequent data processing speed and the reliability are ensured.
Furthermore, it can be determined that the target disease "hypertension" is chronic disease and the target disease "gastric cancer advanced" is malignant disease.
When the type of the target disease is chronic disease, the sequence after the diagnosis of the target disease can be selected from the medical event sequences to be the characteristic subsequence, so that the characteristic subsequence which is more in line with the attention points of doctors and related chronic patients can be obtained, and the related chronic patients can know the evolution trend of the disease conveniently. For example, referring to the explanation associated with step S202, the feature subsequence may be selected from the sequence of medical events corresponding to patient a: hypertension-hyperlipidemia-gastroenteritis. And the integrated data set S G includes a plurality of medical event sequences corresponding to a plurality of patients, a feature subsequence may be selected from the medical event sequences of each patient, and the obtained plurality of feature subsequences may be determined as the target data set. For example, reference may be made to table 1, table 1 schematically showing a target dataset M G (containing a target disease "hypertension") corresponding to a plurality of feature subsequences.
TABLE 1
When the type of the target disease is malignant disease, the sequence before diagnosis of the target disease can be selected from the sequences of the medical events to serve as the characteristic subsequence, so that a preventive reference can be provided for a patient suffering from the related mild disease, and the occurrence of the malignant disease caused by the fact that the patient misses the optimal treatment time due to carelessness when suffering from the mild disease is avoided. For example, referring to the explanation associated with step S202 above, a feature subsequence may be selected from the sequence of medical events corresponding to patient B: dyspepsia-burning sensation in stomach area-advanced gastric cancer. And the integrated data set S W includes a plurality of medical events corresponding to a plurality of patients, a feature subsequence may be selected from the medical event sequences of the respective patients, and the obtained plurality of feature subsequences may be determined as the target data set. For example, reference may be made to table 2, table 2 schematically showing a target dataset M w (containing the target disease "advanced gastric cancer") corresponding to a plurality of feature subsequences.
TABLE 2
In step S130 (step B), a sequence pattern mining is performed on the target data set to obtain a predicted disease sequence corresponding to the target disease.
After the target data set is obtained, sequence pattern mining can be performed on the target data set based on a sequence pattern mining algorithm (such as prefixspan algorithm, GSP algorithm, SPADE algorithm, disc-all algorithm and the like, which can be set according to actual conditions and belong to the protection scope of the disclosure), so as to obtain a plurality of candidate predicted disease sequences corresponding to the target disease. The sequence pattern mining refers to mining frequently-occurring sub-sequences from a large amount of sequence data, and the candidate predicted disease sequences can be frequently-occurring multiple disease sequences.
Then, for example, the plurality of candidate predicted disease sequences mined for the target dataset M G may be "hypertension-hyperlipidemia-gastroenteritis (frequency of occurrence 90%), hypertension-conjunctivitis-gastroenteritis (frequency of occurrence 76%)". For the target dataset M w, the plurality of candidate predicted disease sequences mined may be "dyspepsia-burning in the stomach-gastric cancer advanced stage (frequency of occurrence is 88%), dyspepsia-nausea vomiting-gastric cancer advanced stage (frequency of occurrence is 72%)". Therefore, the development and evolution rules of specific diseases can be rapidly mined, the technical problems of low accuracy and small coverage caused by predicting the evolution trend of the patient disease by only using the personal experience of a clinician in the related technology are solved, the prediction comprehensiveness is improved, a preventive reference is provided for part of early patients, and an effective reference is provided for medical scientific research and clinical treatment.
Furthermore, a frequency threshold (the specific value can be set according to the actual situation, and belongs to the protection scope of the disclosure) can be set, and the candidate predicted disease sequence with the occurrence frequency greater than the frequency threshold is determined as the predicted disease sequence corresponding to the target disease. For example, when the set frequency threshold is 80%, it can be determined that the predicted disease sequence corresponding to the target disease "hypertension" is "hypertension-hyperlipidemia-gastroenteritis", and the predicted disease sequence corresponding to the target disease "gastric cancer advanced stage" is "dyspepsia-gastric burning-gastric cancer advanced stage".
When the set frequency threshold is 70%, the predicted disease sequences corresponding to the target disease "hypertension" can be determined to be "hypertension-hyperlipidemia-gastroenteritis" and "hypertension-conjunctivitis-gastroenteritis". The predicted disease sequences corresponding to the target disease "gastric cancer advanced stage" are "dyspepsia-gastric area burning sensation-gastric cancer advanced stage" and "dyspepsia-nausea and vomiting-gastric cancer advanced stage".
Therefore, when different frequency thresholds are set, different numbers of predicted disease sequences can be obtained, so that comprehensive prediction of the evolution trend of the patient's illness is realized, the patient is helped to predict the evolution trend of the disease in advance, and accordingly, the patient can do things in the aspects of eating habits, sanitary habits, life style and the like, the autoimmune power is enhanced, the occurrence of the disease is prevented, and rapid deterioration of the disease is avoided.
After obtaining the predicted disease sequence corresponding to the target disease, reference may be made to fig. 3, where fig. 3 shows a schematic sub-flowchart of a medical record data processing method according to an exemplary embodiment of the disclosure, specifically, a schematic sub-flowchart of obtaining a new predicted disease sequence, including step S301 to step S303, and a specific embodiment will be explained below with reference to fig. 3.
In step S301, it is determined based on the medical knowledge base whether or not a specified disease exists in the predicted disease sequence, the specified disease being a disease that does not have a pathological association with the target disease.
For example, a medical knowledge base (i.e., a database containing a huge amount of medical knowledge, and exemplary, the database may contain etiology, cases, diagnosis methods, treatment methods, and prevention methods of various diseases, therapeutic drugs for various diseases, usage amounts, precautions, medication descriptions, knowledge of clinical common diseases, knowledge of common drugs, etc.) may be constructed in advance, and whether a specific disease exists in the predicted disease sequence may be determined based on the medical knowledge base, where the specific disease may be a disease that does not have a pathological association relationship with the above-mentioned target disease. For example, when it is determined that the predicted disease sequence corresponding to the target disease "hypertension" is "hypertension-hyperlipidemia-gastroenteritis-cold", it is exemplary that "cold" is a specified disease that does not have a pathological association with the target disease based on the above-mentioned medical knowledge base.
In step S302, if present, the specified disease is removed from the integrated dataset.
Furthermore, the specified disease "cold" may be eliminated from the integrated dataset S G. Therefore, the specified diseases which do not have pathological association relation with the target diseases can be removed, the interference of the specified diseases on the data processing result is avoided, and the reliability of a new predicted disease sequence generated later is ensured.
In step S303, the above steps a and B are repeatedly performed, and a new predicted disease sequence corresponding to the target disease is obtained.
After the specified disease is removed from the integrated dataset, steps a and B (i.e., steps S120 and S130) described above may be repeated to iteratively determine a new predicted disease sequence corresponding to the target disease. Therefore, the reliability of the generated new predicted disease sequence can be improved, and powerful evidence and feasibility guidance are provided for medical research of relevant disease development rules or causes.
For example, referring to fig. 4, fig. 4 is a schematic overall flow chart of a medical record data processing method according to an exemplary embodiment of the disclosure, including steps S401 to S404, and a specific implementation is explained below in conjunction with fig. 4.
In step S401, data processing (namely, sorting medical events in original medical data to obtain a medical event sequence, determining the medical event sequence as medical record data, classifying the medical record data to obtain an integrated data set, determining a target data set from the integrated data set);
in step S402, sequence pattern mining is performed to obtain a predicted disease sequence corresponding to a target disease;
In step S403, a priori knowledge correction (determining whether a specified disease exists in the predicted disease sequence based on the medical knowledge base, culling the specified disease from the integrated dataset), and returning to determining the target dataset from the integrated dataset, and sequence pattern mining the target dataset;
in step S404, the evolution law of the target disease (new predicted disease sequence) is obtained.
Based on the technical scheme, on one hand, the method and the device can remove redundant data which do not accord with disease development conditions aiming at different disease types, and reserve a target data set which accords with the disease development conditions, so that the follow-up data processing speed and the reliability are ensured. On the other hand, the development and evolution rules of specific diseases can be rapidly mined, the technical problems of lower accuracy and smaller coverage caused by predicting the evolution trend of the patient disease by only using the personal experience of a clinician in the related technology are solved, the prediction comprehensiveness is improved, a preventive reference is provided for part of early patients, and an effective reference is provided for medical scientific research and clinical treatment.
The present disclosure further provides a medical record data processing apparatus, and fig. 5 shows a schematic structural diagram of the medical record data processing apparatus in an exemplary embodiment of the present disclosure; as shown in fig. 5, the medical record data processing apparatus 500 can include a categorization processing module 501, a determination module 502, and a sequence pattern mining module 503. Wherein:
the classification processing module 501 is configured to perform classification processing on medical record data including a target disease, so as to obtain an integrated data set.
In an exemplary embodiment of the present disclosure, the classification processing module is configured to obtain a plurality of medical events in raw medical record data corresponding to each patient, and determine a timestamp corresponding to each medical event; sequencing the plurality of medical events according to the sequence from the early to the late of the time stamp to obtain a medical event sequence; the sequence of medical events is determined as medical record data.
In an exemplary embodiment of the present disclosure, the categorizing processing module is further for determining an invalid medical event of the plurality of target medical events when there are the same plurality of target medical events in the sequence of medical events; the invalid medical event is the target medical event occurring for the nth time; rejection processing is carried out on invalid medical events; n is a positive integer greater than 1.
A determining module 502 is configured to determine a target data set from the integrated data set according to the type of the target disease.
In an exemplary embodiment of the present disclosure, the determining module is configured to select a feature subsequence from each sequence of medical events according to a type of the target disease; a target dataset is determined from the plurality of feature subsequences.
In exemplary embodiments of the present disclosure, the types of target diseases include chronic diseases and malignant diseases; the determining module is further used for selecting a sequence after diagnosis of the target disease from the medical event sequences as a characteristic subsequence when the type of the target disease is chronic disease; when the type of the target disease is malignant disease, a sequence before diagnosis of the target disease is selected from the sequence of medical events as a characteristic subsequence.
The sequence pattern mining module 503 is configured to perform sequence pattern mining on the target data set to obtain a predicted disease sequence corresponding to the target disease.
In an exemplary embodiment of the present disclosure, a sequence pattern mining module is configured to perform sequence pattern mining on a target data set based on a sequence pattern mining algorithm, to obtain a plurality of candidate predicted disease sequences corresponding to a target disease; and determining the candidate predicted disease sequences with the occurrence frequency greater than the frequency threshold as predicted disease sequences corresponding to the target disease.
In an exemplary embodiment of the present disclosure, the sequence pattern mining module is further configured to determine, based on the medical knowledge base, whether a specified disease exists in the predicted disease sequence, the specified disease being a disease that does not have a pathological association with the target disease; if so, rejecting the specified disease from the integrated dataset; repeating the steps of determining the target data set and performing sequence pattern mining to obtain a new predicted disease sequence corresponding to the target disease.
The specific details of each module in the medical record data processing device are described in detail in the corresponding medical record data processing method, so that the details are not repeated here.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order, or that all illustrated steps be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer storage medium capable of implementing the above method is also provided. On which a program product is stored which enables the implementation of the method described above in the present specification. In some possible embodiments, the various aspects of the present disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.
Referring to fig. 6, a program product 600 for implementing the above-described method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 700 according to such an embodiment of the present disclosure is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 7, the electronic device 700 is embodied in the form of a general purpose computing device. Components of electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, a bus 730 connecting the different system components (including the memory unit 720 and the processing unit 710), and a display unit 740.
Wherein the storage unit stores program code that is executable by the processing unit 710 such that the processing unit 710 performs steps according to various exemplary embodiments of the present disclosure described in the above-described "exemplary methods" section of the present specification. For example, the processing unit 710 may perform as shown in fig. 1: step S110, classifying and processing the medical record data containing the target diseases to obtain an integrated data set; step S120 (step A), determining a target data set from the integrated data set according to the type of the target disease; step S130 (step B), sequence pattern mining is carried out on the target data set, and a predicted disease sequence corresponding to the target disease is obtained.
The memory unit 720 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 7201 and/or cache memory 7202, and may further include Read Only Memory (ROM) 7203.
The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 730 may be a bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 700 may also communicate with one or more external devices 800 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 700, and/or any device (e.g., router, modem, etc.) that enables the electronic device 700 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 750. Also, electronic device 700 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 760. As shown, network adapter 760 communicates with other modules of electronic device 700 over bus 730. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 700, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Furthermore, the above-described figures are only schematic illustrations of processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (7)
1. A medical record data processing method, comprising:
Acquiring a plurality of medical events in original medical record data corresponding to each patient, and determining a time stamp corresponding to each medical event;
sorting the plurality of medical events according to the sequence from the early to the late of the time stamp to obtain a medical event sequence;
Determining the sequence of medical events as medical record data comprising a disease of interest;
Classifying the medical record data containing the target diseases to obtain an integrated data set;
Step A: determining a target data set from the integrated data set according to the type of the target disease;
The integrated dataset includes a plurality of sequences of medical events corresponding to a plurality of patients; said determining a target dataset from said integrated dataset according to a type of said target disease, comprising: according to the type of the target disease, selecting a characteristic subsequence from each medical event sequence; determining the target data set according to a plurality of the feature subsequences;
Types of the target diseases include chronic diseases and malignant diseases; the selecting a characteristic subsequence from each medical event sequence according to the type of the target disease, includes: when the type of the target disease is chronic disease, selecting a sequence after diagnosis of the target disease from each of the medical event sequences as the characteristic subsequence; when the type of the target disease is malignant disease, selecting a sequence preceding the target disease from the sequences of medical events to be the characteristic subsequence;
and (B) step (B): and performing sequence pattern mining on the target data set to obtain a predicted disease sequence corresponding to the target disease.
2. The method of claim 1, wherein after obtaining the predicted disease sequence corresponding to the target disease, the method further comprises:
judging whether a specified disease exists in the predicted disease sequence based on a medical knowledge base, wherein the specified disease is a disease which does not have a pathological association relation with the target disease;
If so, rejecting the specified disease from the integrated dataset;
and (3) repeatedly executing the step A and the step B to obtain a new predicted disease sequence corresponding to the target disease.
3. The method of claim 1, wherein the sequence pattern mining the target data set to obtain a predicted disease sequence corresponding to the target disease comprises:
performing sequence pattern mining on the target data set based on a sequence pattern mining algorithm to obtain a plurality of candidate predicted disease sequences corresponding to the target disease;
and determining the candidate predicted disease sequence with the occurrence frequency greater than a frequency threshold as the predicted disease sequence corresponding to the target disease.
4. The method of claim 1, wherein after obtaining the sequence of medical events, the method further comprises:
Determining an invalid medical event of the plurality of target medical events when the same plurality of target medical events exist in the medical event sequence; the invalid medical event is the target medical event occurring for the nth time;
Performing rejection processing on the invalid medical event;
N is a positive integer greater than 1.
5. A medical record data processing apparatus, comprising:
The classifying processing module is used for acquiring a plurality of medical events in the original medical record data corresponding to each patient and determining the time stamp corresponding to each medical event; sorting the plurality of medical events according to the sequence from the early to the late of the time stamp to obtain a medical event sequence; determining the sequence of medical events as medical record data comprising a disease of interest; classifying the medical record data containing the target diseases to obtain an integrated data set;
A determining module for determining a target data set from the integrated data set according to the type of the target disease; the integrated dataset includes a plurality of sequences of medical events corresponding to a plurality of patients; the determining module determines a target data set from the integrated data set according to the type of the target disease, comprising: according to the type of the target disease, selecting a characteristic subsequence from each medical event sequence; determining the target data set according to a plurality of the feature subsequences;
Types of the target diseases include chronic diseases and malignant diseases; the determining module selects a characteristic subsequence from each medical event sequence according to the type of the target disease, and the characteristic subsequence comprises: when the type of the target disease is chronic disease, selecting a sequence after diagnosis of the target disease from each of the medical event sequences as the characteristic subsequence; when the type of the target disease is malignant disease, selecting a sequence preceding the target disease from the sequences of medical events to be the characteristic subsequence;
and the sequence pattern mining module is used for mining the sequence pattern of the target data set to obtain a predicted disease sequence corresponding to the target disease.
6. A computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the medical record data processing method according to any one of claims 1 to 4.
7. An electronic device, comprising:
A processor; and
A memory for storing executable instructions of the processor;
Wherein the processor is configured to perform the medical record data processing method of any one of claims 1-4 via execution of the executable instructions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010632725.8A CN111785370B (en) | 2020-07-01 | 2020-07-01 | Medical record data processing method and device, computer storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010632725.8A CN111785370B (en) | 2020-07-01 | 2020-07-01 | Medical record data processing method and device, computer storage medium and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111785370A CN111785370A (en) | 2020-10-16 |
| CN111785370B true CN111785370B (en) | 2024-05-17 |
Family
ID=72759655
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010632725.8A Active CN111785370B (en) | 2020-07-01 | 2020-07-01 | Medical record data processing method and device, computer storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111785370B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113096756B (en) * | 2021-04-26 | 2023-12-22 | 讯飞医疗科技股份有限公司 | Disease evolution classification method, device, electronic equipment and storage medium |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106232831A (en) * | 2013-11-06 | 2016-12-14 | 美国卫生和人力服务部 | Method lymphoma type being carried out Subtypes by expression pattern analysis |
| CN106874632A (en) * | 2017-04-06 | 2017-06-20 | 聂聪 | Slow disease health index intelligent monitoring system |
| CN107851464A (en) * | 2015-08-17 | 2018-03-27 | 西门子保健有限责任公司 | For carrying out the method and system of progression of disease modeling and therapy optimization for individual patient |
| CN109036579A (en) * | 2018-08-22 | 2018-12-18 | 泰康保险集团股份有限公司 | Information forecasting method, device, medium and electronic equipment based on block chain |
| CN109493979A (en) * | 2018-10-23 | 2019-03-19 | 平安科技（深圳）有限公司 | A kind of disease forecasting method and apparatus based on intelligent decision |
| CN109659033A (en) * | 2018-12-18 | 2019-04-19 | 浙江大学 | A kind of chronic disease change of illness state event prediction device based on Recognition with Recurrent Neural Network |
| WO2019132685A1 (en) * | 2017-12-29 | 2019-07-04 | Общество С Ограниченной Ответственностью "Интеллоджик" | Method and system for supporting medical decision making |
| CN110085323A (en) * | 2019-04-19 | 2019-08-02 | 北京工业大学 | A kind of diagnosis and treatment path discovery method based on electronic health record |
| CN110211690A (en) * | 2019-04-19 | 2019-09-06 | 平安科技（深圳）有限公司 | Disease risks prediction technique, device, computer equipment and computer storage medium |
| CN110277172A (en) * | 2019-06-27 | 2019-09-24 | 齐鲁工业大学 | A kind of clinical application behavior analysis system and its working method based on efficient negative sequence mining mode |
| CN110459324A (en) * | 2019-06-27 | 2019-11-15 | 平安科技（深圳）有限公司 | Disease forecasting method, apparatus and computer equipment based on shot and long term memory models |
| CN110781216A (en) * | 2019-11-05 | 2020-02-11 | 广东工业大学 | Traditional Chinese medicine symptom association rule mining method and device and storage medium |
| CN111269945A (en) * | 2020-02-25 | 2020-06-12 | 四川省人民医院 | Method for constructing retinal pigment degeneration disease model by using Gm20541 gene and application |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8170888B2 (en) * | 2006-02-13 | 2012-05-01 | Silverman David G | Method and system for assessing, quantifying, coding and communicating a patient's health and perioperative risk |
| JP6420543B2 (en) * | 2011-01-19 | 2018-11-07 | コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． | Genome data processing method |
| US10963795B2 (en) * | 2015-04-28 | 2021-03-30 | International Business Machines Corporation | Determining a risk score using a predictive model and medical model data |
-
2020
- 2020-07-01 CN CN202010632725.8A patent/CN111785370B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106232831A (en) * | 2013-11-06 | 2016-12-14 | 美国卫生和人力服务部 | Method lymphoma type being carried out Subtypes by expression pattern analysis |
| CN107851464A (en) * | 2015-08-17 | 2018-03-27 | 西门子保健有限责任公司 | For carrying out the method and system of progression of disease modeling and therapy optimization for individual patient |
| CN106874632A (en) * | 2017-04-06 | 2017-06-20 | 聂聪 | Slow disease health index intelligent monitoring system |
| WO2019132685A1 (en) * | 2017-12-29 | 2019-07-04 | Общество С Ограниченной Ответственностью "Интеллоджик" | Method and system for supporting medical decision making |
| CN109036579A (en) * | 2018-08-22 | 2018-12-18 | 泰康保险集团股份有限公司 | Information forecasting method, device, medium and electronic equipment based on block chain |
| CN109493979A (en) * | 2018-10-23 | 2019-03-19 | 平安科技（深圳）有限公司 | A kind of disease forecasting method and apparatus based on intelligent decision |
| CN109659033A (en) * | 2018-12-18 | 2019-04-19 | 浙江大学 | A kind of chronic disease change of illness state event prediction device based on Recognition with Recurrent Neural Network |
| CN110085323A (en) * | 2019-04-19 | 2019-08-02 | 北京工业大学 | A kind of diagnosis and treatment path discovery method based on electronic health record |
| CN110211690A (en) * | 2019-04-19 | 2019-09-06 | 平安科技（深圳）有限公司 | Disease risks prediction technique, device, computer equipment and computer storage medium |
| CN110277172A (en) * | 2019-06-27 | 2019-09-24 | 齐鲁工业大学 | A kind of clinical application behavior analysis system and its working method based on efficient negative sequence mining mode |
| CN110459324A (en) * | 2019-06-27 | 2019-11-15 | 平安科技（深圳）有限公司 | Disease forecasting method, apparatus and computer equipment based on shot and long term memory models |
| CN110781216A (en) * | 2019-11-05 | 2020-02-11 | 广东工业大学 | Traditional Chinese medicine symptom association rule mining method and device and storage medium |
| CN111269945A (en) * | 2020-02-25 | 2020-06-12 | 四川省人民医院 | Method for constructing retinal pigment degeneration disease model by using Gm20541 gene and application |
Non-Patent Citations (2)
| Title |
|---|
| 时间序列相似性与预测算法研究及其应用;闫明月;博士电子期刊;1-124 * |
| 疾病时间序列研究中预测模型的应用;吴本璞;浙江医科大学学报;第第18卷卷(第第6期期);277-281 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111785370A (en) | 2020-10-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8930223B2 (en) | Patient cohort matching | |
| Nabi et al. | Coronary artery calcium scoring in the emergency department: identifying which patients with chest pain can be safely discharged home | |
| Salod et al. | Comparison of the performance of machine learning algorithms in breast cancer screening and detection: A protocol | |
| JP2005508557A (en) | Patient data mining | |
| Rannikmäe et al. | Accuracy of identifying incident stroke cases from linked health care data in UK Biobank | |
| Phillips-Wren et al. | Mining lung cancer patient data to assess healthcare resource utilization | |
| WO2008016530A2 (en) | Computer aided detection and decision support | |
| US20190237200A1 (en) | Recording medium recording similar case retrieval program, information processing apparatus, and similar case retrieval method | |
| Persson et al. | A machine learning sepsis prediction algorithm for intended intensive care unit use (NAVOY Sepsis): proof-of-concept study | |
| US20170270250A1 (en) | Building a patient's medical history from disparate information sources | |
| CN114300141A (en) | Disease risk grade prediction method and device, readable storage medium and electronic equipment | |
| WO2014151626A1 (en) | Electronic variant classification | |
| Farjah et al. | An automated method for identifying individuals with a lung nodule can be feasibly implemented across health systems | |
| CN111785370B (en) | Medical record data processing method and device, computer storage medium and electronic equipment | |
| Surodina et al. | Machine learning for risk group identification and user data collection in a herpes simplex virus patient registry: algorithm development and validation study | |
| Ma et al. | Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients | |
| US7324918B1 (en) | Forecasting outcomes based on analysis of text strings | |
| Spielvogel et al. | Diagnosis and prognosis of abnormal cardiac scintigraphy uptake suggestive of cardiac amyloidosis using artificial intelligence: a retrospective, international, multicentre, cross-tracer development and validation study | |
| Pokala et al. | Analysis and Comparison for Prediction of Diabetic among Pregnant Women using Innovative Support Vector Machine Algorithm over Random Forest Algorithm with Improved Accuracy | |
| Crespin et al. | Improved diagnostic performance of insertable cardiac monitors by an artificial intelligence-based algorithm | |
| Hyde et al. | A claims‐based, machine‐learning algorithm to identify patients with pulmonary arterial hypertension | |
| Kakulapati et al. | Self-aware COVID-19 AI approach (SIntL-CoV19) by integrating infected scans with internal behavioral analysis | |
| Ling et al. | A semi-supervised machine learning approach to detecting recurrent metastatic breast cancer cases using linked cancer registry and electronic medical record data | |
| Wang et al. | Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models | |
| Hasannejadasl et al. | A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer |
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 |