CN111383726B - Electronic medical record data processing method and device, electronic equipment and readable medium - Google Patents
Electronic medical record data processing method and device, electronic equipment and readable medium Download PDFInfo
- Publication number
- CN111383726B CN111383726B CN201811624762.3A CN201811624762A CN111383726B CN 111383726 B CN111383726 B CN 111383726B CN 201811624762 A CN201811624762 A CN 201811624762A CN 111383726 B CN111383726 B CN 111383726B
- Authority
- CN
- China
- Prior art keywords
- data
- drug
- target
- medical record
- rule
- 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
Images
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
- 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
-
- 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
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Biomedical Technology (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The present disclosure relates to a method, an apparatus, an electronic device, and a computer readable medium for processing electronic medical record data. The method comprises the following steps: extracting feature data and a corresponding time tag from target electronic medical record data, wherein the feature data comprises: basic data, diagnostic data, inspection data, medication data, and test data; judging whether the characteristic data meets a preset condition or not based on the time tag; when a preset condition is met, inputting the characteristic data into a drug damage risk model, and determining the drug damage probability of each drug in the drug data; determining the medicine with the maximum medicine damage probability as a target medicine; the drug damage assessment model is generated by training a machine learning model through the characteristic data of the diagnosed drug damage. The method and the device can help clinicians to quickly locate high-value clinical diagnosis information, and effectively improve liver injury identification speed.
Description
Technical Field
The present disclosure relates to the field of medical information processing, and in particular, to a method and apparatus for processing electronic medical record data, an electronic device, and a computer readable medium.
Background
Currently, the national drug adverse reaction (Adverse Drug Reaction, ADR) monitoring platform used in China is a system based on a spontaneous report mode, and ADR case reports from national medical institutions (monitoring subjects), drug manufacturers and business enterprises are passively collected. The ADR monitoring work in the existing medical institution is mainly carried out in a manual filling mode, depends on the enthusiasm of pharmacists or doctors for filling, is a passive monitoring mode, and has the problems of ADR condition missing report, false report, delayed report and the like, and needs to be further solved.
The active monitoring technology of adverse drug reaction mainly comprises the steps of establishing a rule trigger library for active monitoring through a trigger technology. The conventional data collection means passively collect ADR case reports from national medical institutions (monitoring subjects), pharmaceutical manufacturers and business enterprises in a manual reporting manner. The existing adverse drug reactions are mostly obtained based on sensors or through statistical analysis of paper medical records, and the discovery approach mainly depends on clinical experience of clinicians on the diseases, and the means are all scenes of knowledge combination and retrieval of clinicians when familiar clinical problems are encountered. The value of the implementation of the above approach in a personalized diverse real clinical scenario is very limited.
Accordingly, there is a need for a new electronic medical record data processing method, apparatus, electronic device, and computer-readable medium.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of the above, the present disclosure provides a method, an apparatus, an electronic device, and a computer readable medium for processing electronic medical record data, which can help a clinician to quickly locate high-value clinical diagnosis information, and effectively improve liver injury identification speed.
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 an aspect of the present disclosure, there is provided a method for processing electronic medical record data, the method including: extracting feature data and a corresponding time tag from target electronic medical record data, wherein the feature data comprises: basic data, diagnostic data, inspection data, medication data, and test data; judging whether the characteristic data meets a preset condition or not based on the time tag; when a preset condition is met, inputting the characteristic data into a drug damage risk model, and determining the drug damage probability of each drug in the drug data; determining the medicine with the maximum medicine damage probability as a target medicine; the drug damage assessment model is generated by training a machine learning model through the characteristic data of the diagnosed drug damage.
In an exemplary embodiment of the present disclosure, extracting, from the target electronic medical record data, the time stamp corresponding to the feature data includes: acquiring an original electronic medical record in real time; acquiring target disease related data from the original electronic medical record through a structuring method; and extracting the characteristic data and the corresponding time tag from the target disease related data.
In an exemplary embodiment of the present disclosure, determining whether the feature data satisfies a predetermined condition based on the time tag includes: determining at least one target time tag according to a predetermined rule; dividing the feature data into at least one feature set based on the at least one target time stamp; determining a target rule corresponding to the at least one feature set; and determining whether the feature data satisfies a predetermined condition based on the target rule and the at least one feature set.
In an exemplary embodiment of the present disclosure, determining whether the feature data satisfies a predetermined condition based on the time tag further includes: generating the target rule by a diagnostic specification of a target disease; the target rule is divided into a front rule and a rear rule.
In an exemplary embodiment of the present disclosure, determining whether the feature data satisfies a predetermined condition based on the target rule and the at least one feature set includes: determining time association relations among basic data, diagnosis data, inspection data, medication data and inspection data in at least one feature set; judging whether the at least one feature set meets a pre-set rule and a post-set rule of the target rule based on the time association relation; and determining that the feature set meets a predetermined condition when the at least one feature set meets a pre-rule and a post-rule of the target rule.
In an exemplary embodiment of the present disclosure, after determining the drug with the highest probability of drug damage as the target drug, further includes: and updating a medicine disease database according to the target medicine and the medicine damage probability corresponding to the target medicine.
In an exemplary embodiment of the present disclosure, after determining the drug with the highest probability of drug damage as the target drug, further includes: extracting electronic medical record data similar to the target electronic medical record data from an electronic case library; and sending early warning information to the similar electronic medical record data.
According to an aspect of the present disclosure, there is provided an electronic medical record data processing apparatus, including: the extraction module is used for extracting the characteristic data and the corresponding time tag from the target electronic medical record data, wherein the characteristic data comprises: basic data, diagnostic data, inspection data, medication data, and test data; the judging module is used for judging whether the characteristic data meets a preset condition or not based on the time tag; the identification module is used for inputting the characteristic data into a drug damage risk model when a preset condition is met, and determining the drug damage probability of each drug in the drug data; the drug damage evaluation model is generated by training a machine learning model file by the characteristic data of the diagnosed drug damage; and the processing module is used for determining the medicine with the highest medicine damage probability as the target medicine for subsequent processing.
According to an aspect of the present disclosure, there is provided an electronic device including: one or more processors; a storage means for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the methods as described above.
According to an aspect of the present disclosure, a computer-readable medium is presented, on which a computer program is stored, which program, when being executed by a processor, implements a method as described above.
According to the electronic medical record data processing method, the electronic equipment and the computer readable medium, the basic data, the diagnosis data, the inspection data, the medication data and the inspection data of a patient are input into a drug damage risk model to determine the drug damage probability of each drug in the medication data of the patient; and the medicine with the highest medicine damage probability is determined as the target medicine causing the medicine adverse reaction, so that a clinician can be helped to quickly locate high-value clinical diagnosis information, and the liver damage identification speed is effectively improved.
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 above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely examples of the present disclosure and other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 is a system scenario block diagram illustrating a method and an apparatus for processing electronic medical record data according to an exemplary embodiment.
Fig. 2 is a flowchart illustrating a method of electronic medical record data processing according to an exemplary embodiment.
Fig. 3 is a flowchart illustrating a method of electronic medical record data processing according to another exemplary embodiment.
Fig. 4 is a block diagram of an electronic medical record data processing device, according to an example embodiment.
Fig. 5 is a block diagram of an electronic device, according to an example embodiment.
FIG. 6 is a schematic diagram illustrating a computer-readable storage medium according to an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments 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 same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.
Furthermore, 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 disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another element. Accordingly, a first component discussed below could be termed a second component without departing from the teachings of the concepts of the present disclosure. As used herein, the term "and/or" includes any one of the associated listed items and all combinations of one or more.
Those skilled in the art will appreciate that the drawings are schematic representations of example embodiments and that the modules or flows in the drawings are not necessarily required to practice the present disclosure, and therefore, should not be taken to limit the scope of the present disclosure.
The drug-induced liver injury (DILI) refers to liver injury induced by various prescription or non-prescription chemicals, biological agents, traditional Chinese Medicine (TCM), natural Medicine (NM), health Product (HP), dietary Supplement (DS) and its metabolites and adjuvants. TCM refers to various herbal and non-herbal Chinese medicinal materials, decoction pieces and compound Chinese patent medicines which are produced and used under the guidance of traditional national medical theory of Chinese medicine and the like, and NM refers to natural medicinal substances and preparations prepared by applying modern medical theory and technology. DILI is one of the most common and severe Adverse Drug Reactions (ADR), and severe patients can cause Acute Liver Failure (ALF) and even death. So far, simple, objective and specific diagnostic indexes and specific treatment means are still lacking.
The signal discovery of drug-induced liver injury presents difficulties. The existing active monitoring method has problems and defects, namely: in the design of an active monitoring method, only partial influencing factors are often considered, the method cannot be used for finding and monitoring new signals, and the reliability and the accuracy of the result are relatively low; and two,: in the application of technical means, trigger technology is widely used, however, the trigger has the limitation of being incapable of retrieving medical record texts, and the application of new technology needs to be considered; and thirdly,: even if a related suspected case/event is found, the correlation cannot be evaluated directly by quantitative/semi-quantitative means such as a scale score.
Without loss of generality, a specific description of an embodiment will be given in this disclosure with drug-induced liver injury as one specific instance of drug adverse reactions.
FIG. 1 is a system block diagram illustrating a method and apparatus for processing electronic medical record data according to an exemplary embodiment.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a medical data processing class application, a web browser application, a search class application, an instant messaging tool, a mailbox client, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The user may generate electronic medical record data through the terminal device 101, 102, 103, and the terminal device 101, 102, 103 may extract, for example, feature data and a time tag corresponding to the feature data from the target electronic medical record data, where the feature data includes: basic data, diagnostic data, inspection data, medication data, and test data; the terminal device 101, 102, 103 may determine whether the feature data satisfies a predetermined condition, for example, based on the time tag; and the terminal device 101, 102, 103 may, for example, input the feature data into a drug damage risk model when a predetermined condition is satisfied, determine a drug damage probability of each drug in the drug data, wherein the drug damage evaluation model is generated by training a machine learning model through feature data of a diagnosed drug damage; the terminal devices 101, 102, 103 may, for example, determine the drug with the highest probability of drug damage as the target drug.
The server 105 may be a server that provides various services, such as a background server that provides support for electronic medical record data generated by a user using the terminal devices 101, 102, 103. The server 105 may analyze the received electronic medical record data, and feed back the target medicines that may cause the drug damage and are in the electronic medical record data to the terminal device.
The user may generate electronic medical record data through the terminal device 101, 102, 103, the terminal device 101, 102, 103 may forward the electronic medical record data to the server 105, and the server 105 may extract, for example, feature data and a time tag corresponding to the feature data from the target electronic medical record data, where the feature data includes: basic data, diagnostic data, inspection data, medication data, and test data; the server 105 may determine whether the feature data satisfies a predetermined condition, for example, based on the time stamp; the server 105 may, for example, input the feature data into a drug damage risk model when a predetermined condition is satisfied, determine a drug damage probability of each drug in the drug data, wherein the drug damage assessment model is generated by training a machine learning model through feature data of diagnosed drug damage; the server 105 may also determine, for example, the drug with the highest probability of drug damage as the target drug. The server 105 may also for example return the target drug to the terminal device 101, 102, 103.
The server 105 may be an entity server, or may be a plurality of servers, for example, it should be noted that the electronic medical record data processing method provided in the embodiment of the present disclosure may be executed by the server 105 and/or the terminal devices 101, 102, 103, and accordingly, the electronic medical record data processing apparatus may be disposed in the server 105 and/or the terminal devices 101, 102, 103.
According to the electronic medical record data processing method and device, through data processing and application technology, evaluation contents are effectively integrated, treatment standards and evidence of real medical record data are combined, a clinician is helped to quickly locate high-value clinical diagnosis information, and liver injury identification speed is effectively improved.
According to the electronic medical record data processing method and device, the liver injury clinical real disease model caused by the medicine is stored, the actual clinic is dynamically corrected according to the medicine liver injury identification process, and the subsequent automatic monitoring function is continuously optimized
Fig. 2 is a flowchart illustrating a method of electronic medical record data processing according to an exemplary embodiment. The electronic medical record data processing method 20 at least includes steps S202 to S208.
As shown in fig. 2, in S202, feature data and a time tag corresponding to the feature data are extracted from target electronic medical record data, where the feature data includes: basic data, diagnostic data, inspection data, medication data, and test data.
In one embodiment, extracting the feature data and the corresponding time tag from the target electronic medical record data includes: acquiring an original electronic medical record in real time; acquiring target disease related data from the original electronic medical record through a structuring method; and extracting the characteristic data and the corresponding time tag from the target disease related data.
In particular, data may be acquired quickly, for example, depending on real-time structuring methods of real-world data. For example, a real-time platform data stream may be first established, and when new real-world medical data is generated, the large data platform will generate relevant information in real time through a structuring algorithm; and secondly, the correlation between the related information and the specific field path is specified in advance, and the related disease information is acquired from a big data platform in real time in the system, so that the trouble of manual input is avoided. The manual evaluation of the full page content can quickly help doctors to locate key information and reduce invalid information input.
In S204, it is determined whether the feature data satisfies a predetermined condition based on the time stamp.
In one embodiment, determining whether the feature data satisfies a predetermined condition based on the time tag includes: determining at least one target time tag according to a predetermined rule; dividing the feature data into at least one feature set based on the at least one target time stamp; determining a target rule corresponding to the at least one feature set; and determining whether the feature data satisfies a predetermined condition based on the target rule and the at least one feature set.
The specific content of determining whether the feature data satisfies a predetermined condition based on the time stamp will be described in detail in the embodiment corresponding to fig. 3.
In S206, when a predetermined condition is satisfied, the feature data is input into a drug damage risk model, and a drug damage probability of each drug in the drug data is determined. The drug damage assessment model is generated by training a machine learning model file through feature data of the diagnosed drug damage.
The machine learning module machine learning model in the disclosure is divided into two major categories of supervised learning and unsupervised learning according to usable data types.
More specific supervised learning may include machine learning models for classification and for regression, among others:
1) Classification machine learning model: linear classifiers (e.g., LR), support Vector Machines (SVM), naive Bayes (NB), K Nearest Neighbor (KNN), decision Tree (DT), integrated models (RF/GDBT, etc.)
2) Regression machine learning model: linear regression, support Vector Machine (SVM), K Nearest Neighbor (KNN), regression tree (DT), integration model (ExtraTrees/RF/GDBT)
Unsupervised learning mainly includes: data clustering (K-means)/data dimension reduction (PCA), and the like.
The specific characteristic data can be extracted from the electronic medical record data of the confirmed drug injury, and the specific characteristic data can be the basic information data, the diagnosis data, the examination data, the medication order data, the examination data and other relevant disease information of the patient after the sensitive information is removed. And carrying out data standardization and normalization processing on the various information, and then respectively establishing corresponding association relations between basic information data, diagnosis data, inspection data, medication data and time.
And according to the corresponding relation, the multiple factors are summarized in a multi-time axis mode, and the summarized characteristic data are used as training data and input into a machine learning model.
And extracting the specific medicine name causing the medicine injury from the electronic medical record data of the confirmed medicine injury, and inputting the specific medicine name into a machine learning model as tag data corresponding to the training data.
Training the machine learning model through the plurality of characteristic data and the corresponding labels, and finally obtaining the drug damage risk model. The drug damage risk model can be used for assisting in judging and identifying the association relation between the drug and the liver damage.
In S208, the drug having the highest probability of drug damage is determined as the target drug. After the target drug is determined, the method may further include: updating a drug disease database according to the target drug and the drug damage probability corresponding to the target drug; may further comprise: extracting electronic medical record data similar to the target electronic medical record data from an electronic case library; and sending early warning information to the similar electronic medical record data.
In one embodiment, after medical personnel can complete the drug liver injury relevance evaluation and complete the input of all decision information, the results of the drug possibly causing drug injury can be generated, the related result data can also update the corresponding drug disease database, and the related risk data is updated, so that a more perfect drug reasonable use knowledge base is provided for clinical use of the drug.
In one embodiment, for example, a medical record similar to the data in the currently input electronic medical record can be automatically found, and the adverse reaction early warning is performed, so that the similar adverse reaction is avoided.
The aim of the present disclosure is to establish a system for collecting, evaluating, predicting and early warning drug-induced liver injury diseases. According to the electronic medical record data processing method disclosed by the invention, a supervision and management department and a platform cooperation hospital related personnel login platform can be supported to conduct drug liver injury subject research by using related authorized data, and the method is used for evaluating and comparing the known high-risk drug liver injury situation again, finding new drug liver injury monitoring signals, drug liver injury influence differences among different drugs and the like, and reducing the clinical probability of a new drug liver injury event and the like.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 3 is a flowchart of a method, an apparatus, an electronic device, and a computer readable medium for processing electronic medical record data according to another exemplary embodiment. The flow 30 shown in fig. 3 is a detailed description of S204 "determining whether the feature data satisfies a predetermined condition based on the time stamp" in the flow shown in fig. 2,
as shown in fig. 3, at least one target time stamp is determined according to a predetermined rule in S302.
In one embodiment, the differential diagnostic specification (index threshold) of drug-induced liver injury may be converted to machine language. This step can be achieved by defining and configuring the background of the drug-induced liver injury signal. First, the relevant clinical standard guidelines are generally used in clinical differential diagnosis to form rule contents that can be translated. The corresponding file is translated into a machine-recognizable code, and the relevant authentication rules can be quickly mechanized by the translation device. A series of configuration operations can be performed, and the requirements of different personalized scenes at the product level are met. The hit medical records meeting the logic requirements in the database can be subjected to queue construction through a preset rule and used for subsequent manual evaluation correction and labeling.
Specifically, for example, the index a suddenly rises as a predetermined rule, and the time when the index a suddenly rises may be further used as a target time stamp; furthermore, the sudden decrease of the index B may be used as a predetermined rule, and the time when the index B suddenly decreases may be used as a target time stamp, which is to be noted that the number of target time stamps is not limited.
In S304, the feature data is divided into at least one feature set based on the at least one target time stamp. In the above, the data normalization processing is performed on the patient basic information data, the diagnosis data, the examination data, the medication order data, the examination data, and other disease-related information. And establishing an association relation between basic information data, diagnosis data, inspection data and medication data and time.
The patient's characteristic data is divided into different sets according to the label of the target time, and for example, the time in the case data includes: a, B, C, D, E; wherein B and C are determined as target time tags, the feature data may be divided into 2 groups, a first set of features comprising feature data for a, B times, and a second set comprising feature data for C, D, E sets.
In S306, a target rule corresponding to the at least one feature set is determined. Wherein generating the target rule by a diagnostic specification of a target disease is further included; the target rule is divided into a front rule and a rear rule.
In S308, it is determined whether the feature data satisfies a predetermined condition based on the target rule and the at least one feature set. May include: determining time association relations among basic data, diagnosis data, inspection data, medication data and inspection data in at least one feature set; judging whether the at least one feature set meets a pre-set rule and a post-set rule of the target rule based on the time association relation; and determining that the feature set meets a predetermined condition when the at least one feature set meets a pre-rule and a post-rule of the target rule.
In one embodiment, alanine Aminotransferase (ALT) is at a labeling parameter, e.g., a normally high value (ULN), before time B, and exceeds a set threshold, e.g., a sudden abnormal rise by a factor of several, after time B, before and after which time it is determined whether the alanine aminotransferase meets its medical characterization rules, respectively, and the feature set is determined to be suitable for meeting the predetermined condition.
Those skilled in the art will appreciate that all or part of the steps implementing the above described embodiments are implemented as a computer program executed by a CPU. The above-described functions defined by the above-described methods provided by the present disclosure are performed when the computer program is executed by a CPU. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic disk or an optical disk, etc.
Furthermore, it should be noted that the above-described figures are merely illustrative of the processes involved 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.
The following are device embodiments of the present disclosure that may be used to perform method embodiments of the present disclosure. For details not disclosed in the embodiments of the apparatus of the present disclosure, please refer to the embodiments of the method of the present disclosure.
Fig. 4 is a block diagram of an electronic medical record data processing device, according to an example embodiment. The electronic medical record data processing device 40 includes: the system comprises an extraction module 402, a judgment module 404, an identification module 406 and a processing module 408.
The extracting module 402 is configured to extract, from the target electronic medical record data, feature data and a time tag corresponding to the feature data, where the feature data includes: basic data, diagnostic data, inspection data, medication data, and test data; may include: comprising the following steps: acquiring an original electronic medical record in real time; acquiring target disease related data from the original electronic medical record through a structuring method; and extracting the characteristic data and the corresponding time tag from the target disease related data.
The judging module 404 is configured to judge whether the feature data meets a predetermined condition based on the time tag; comprising the following steps: determining at least one target time tag according to a predetermined rule; dividing the feature data into at least one feature set based on the at least one target time stamp; determining a target rule corresponding to the at least one feature set; and determining whether the feature data satisfies a predetermined condition based on the target rule and the at least one feature set.
The identification module 406 is configured to input the feature data into a drug damage risk model when a predetermined condition is satisfied, and determine a drug damage probability of each drug in the drug data; the drug damage evaluation model is generated by training a machine learning model file by the characteristic data of the diagnosed drug damage; and
The processing module 408 is configured to determine the drug with the highest drug damage probability as the target drug for subsequent processing. The method specifically comprises the following steps: extracting electronic medical record data similar to the target electronic medical record data from an electronic case library; and sending early warning information to the similar electronic medical record data.
According to the electronic medical record data processing device disclosed by the invention, a supervision and management department and a platform cooperation hospital related personnel login platform can be supported to conduct drug liver injury subject research by using related authorized data, and the device is used for evaluating and comparing the known high-risk drug liver injury situation, finding new drug liver injury monitoring signals, drug liver injury influence differences among different drugs and the like, and reducing the clinical probability of newly-increased drug liver injury events and the like.
Fig. 5 is a block diagram of an electronic device, according to an example embodiment.
An electronic device 200 according to such an embodiment of the present disclosure is described below with reference to fig. 5. The electronic device 200 shown in fig. 5 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. 5, the electronic device 200 is in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting the different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.
Wherein the storage unit stores program code executable by the processing unit 210 such that the processing unit 210 performs steps according to various exemplary embodiments of the present disclosure described in the above-described electronic prescription flow processing methods section of the present specification. For example, the processing unit 210 may perform the steps as shown in fig. 2, 3.
The memory unit 220 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 2201 and/or cache memory 2202, and may further include Read Only Memory (ROM) 2203.
The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 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.
The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 200, and/or any device (e.g., router, modem, etc.) that enables the electronic device 200 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 250. Also, the electronic device 200 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 a network adapter 260. Network adapter 260 may communicate with other modules of electronic device 200 via bus 230. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 200, 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, or a network device, etc.) to perform the above-described method according to the embodiments of the present disclosure.
Fig. 6 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the present disclosure.
Referring to fig. 6, a program product 400 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 storage 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 storage medium may also be any readable medium 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 storage 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).
The computer-readable medium carries one or more programs, which when executed by one of the devices, cause the computer-readable medium to perform the functions of: extracting feature data and a corresponding time tag from target electronic medical record data, wherein the feature data comprises: basic data, diagnostic data, inspection data, medication data, and test data; judging whether the characteristic data meets a preset condition or not based on the time tag; when a preset condition is met, inputting the characteristic data into a drug damage risk model, and determining the drug damage probability of each drug in the drug data; and determining the drug with the highest drug damage probability as a target drug for subsequent treatment; the drug damage assessment model is generated by training a machine learning model file through feature data of the diagnosed drug damage.
Those skilled in the art will appreciate that the modules may be distributed throughout several devices as described in the embodiments, and that corresponding variations may be implemented in one or more devices that are unique to the embodiments. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
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 in combination with the necessary hardware. Thus, the technical solutions 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, and include 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.
Those skilled in the art will readily appreciate from the foregoing detailed description that the electronic medical record data processing methods, apparatus, electronic devices, and computer readable media according to embodiments of the present disclosure have one or more of the following advantages.
According to the electronic medical record data processing method disclosed by the embodiment of the invention, a standardized regularization method for identifying and diagnosing the drug-induced liver injury can be provided, and the identification rule of the drug-induced liver injury is converted into a machine language and is integrated into a medical working link, so that the effects of providing decision information support and improving diagnosis and treatment decision efficiency are achieved.
According to the electronic medical record data processing method disclosed by the embodiment of the invention, the evaluation content can be effectively integrated through the data processing and application technology, the treatment standard and the evidence of the real medical record data are combined, the clinician is helped to quickly locate high-value clinical diagnosis information, and the liver injury identification speed is effectively improved.
According to the electronic medical record data processing method disclosed by the embodiment of the disclosure, the liver injury clinical real disease model caused by the medicine can be stored through the system, the actual clinic is dynamically corrected to the medicine liver injury identification flow, and the subsequent automatic monitoring function is continuously optimized.
According to the electronic medical record data processing method disclosed by the embodiment of the invention, the visualization of the drug liver injury differential diagnosis decision can be realized through the system, and a rapid visual analysis tool is provided for clinical retrospective analysis and judgment.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that this disclosure is not limited to the particular arrangements, instrumentalities and methods of implementation described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
In addition, the structures, proportions, sizes, etc. shown in the drawings in the specification are used for the understanding and reading of the disclosure, and are not intended to limit the applicable limitations of the disclosure, so that any structural modification, change in proportion, or adjustment of size is not technically significant, and yet falls within the scope of the disclosure without affecting the technical effects and the objects that can be achieved by the disclosure. Meanwhile, the terms such as "upper", "first", "second", and "a" and the like recited in the present specification are also for convenience of description only, and are not intended to limit the scope of the disclosure, in which the relative relationship changes or modifications thereof are not limited to essential changes in technical content, but are also regarded as the scope of the disclosure.
Claims (9)
1. The electronic medical record data processing method is characterized by comprising the following steps of:
extracting feature data and a corresponding time tag from target electronic medical record data, wherein the feature data comprises: basic data, diagnostic data, inspection data, medication data, and test data;
Judging whether the characteristic data meets a preset condition or not based on the time tag;
when a preset condition is met, inputting the characteristic data into a drug damage risk model, and determining the drug damage probability of each drug in the drug data; and
determining the medicine with the maximum medicine damage probability as a target medicine;
the drug damage evaluation model is generated by training a machine learning model through the characteristic data of the diagnosed drug damage;
wherein the determining whether the feature data satisfies a predetermined condition based on the time tag includes: determining at least one target time tag according to a predetermined rule; dividing the feature data into at least one feature set based on the at least one target time stamp; determining a target rule corresponding to the at least one feature set; and determining whether the feature data satisfies a predetermined condition based on the target rule and the at least one feature set.
2. The method of claim 1, wherein extracting the time stamp corresponding to the feature data from the target electronic medical record data comprises:
acquiring an original electronic medical record in real time;
acquiring target disease related data from the original electronic medical record through a structuring method; and
And extracting the characteristic data and the corresponding time tag from the target disease related data.
3. The method of claim 1, wherein determining whether the characteristic data satisfies a predetermined condition based on the time stamp further comprises:
generating the target rule by a diagnostic specification of a target disease; the target rule is divided into a front rule and a rear rule.
4. The method of claim 3, wherein determining whether the feature data satisfies a predetermined condition based on the target rule and the at least one feature set comprises:
determining time association relations among basic data, diagnosis data, inspection data, medication data and inspection data in at least one feature set;
judging whether the at least one feature set meets a pre-set rule and a post-set rule of the target rule based on the time association relation; and
and when the at least one feature set meets the pre-set rule and the post-set rule of the target rule, determining that the feature set meets a preset condition.
5. The method of claim 1, wherein determining the drug with the greatest probability of drug damage as the target drug further comprises:
And updating a medicine disease database according to the target medicine and the medicine damage probability corresponding to the target medicine.
6. The method of claim 1, wherein determining the drug with the greatest probability of drug damage as the target drug further comprises:
extracting electronic medical record data similar to the target electronic medical record data from an electronic case library; and
and sending early warning information to the similar electronic medical record data.
7. An electronic medical record data processing device, comprising:
the extraction module is used for extracting the characteristic data and the corresponding time tag from the target electronic medical record data, wherein the characteristic data comprises: basic data, diagnostic data, inspection data, medication data, and test data;
the judging module is used for judging whether the characteristic data meets a preset condition or not based on the time tag;
the identification module is used for inputting the characteristic data into a drug damage risk model when a preset condition is met, and determining the drug damage probability of each drug in the drug data; the drug damage evaluation model is generated by training a machine learning model through the characteristic data of the diagnosed drug damage; and
The processing module is used for determining the medicine with the largest medicine damage probability as the target medicine;
the judging module is further used for determining at least one target time tag according to a preset rule; dividing the feature data into at least one feature set based on the at least one target time stamp; determining a target rule corresponding to the at least one feature set; and determining whether the feature data satisfies a predetermined condition based on the target rule and the at least one feature set.
8. An electronic device, comprising:
one or more processors;
a storage means for storing one or more programs;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-6.
9. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811624762.3A CN111383726B (en) | 2018-12-28 | 2018-12-28 | Electronic medical record data processing method and device, electronic equipment and readable medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811624762.3A CN111383726B (en) | 2018-12-28 | 2018-12-28 | Electronic medical record data processing method and device, electronic equipment and readable medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111383726A CN111383726A (en) | 2020-07-07 |
| CN111383726B true CN111383726B (en) | 2023-06-27 |
Family
ID=71216437
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811624762.3A Active CN111383726B (en) | 2018-12-28 | 2018-12-28 | Electronic medical record data processing method and device, electronic equipment and readable medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111383726B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112366002A (en) * | 2020-10-20 | 2021-02-12 | 武汉华大基因科技有限公司 | Method for evaluating unhealthy medicine in real world in hospital based on knowledge base |
| CN112366003A (en) * | 2020-10-20 | 2021-02-12 | 武汉华大基因科技有限公司 | Adverse reaction monitoring method based on clinical real world time series elements |
| CN117476231A (en) * | 2023-11-09 | 2024-01-30 | 中山大学孙逸仙纪念医院 | Machine learning-based pharmaceutical liver injury risk prediction method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005316967A (en) * | 2004-03-31 | 2005-11-10 | Yuyama Manufacturing Co Ltd | Electronic medical chart device |
| JP2008217291A (en) * | 2007-03-02 | 2008-09-18 | Fujifilm Corp | Electronic medical record device and food instruction support method |
| JP2009146345A (en) * | 2007-12-18 | 2009-07-02 | Mitsubishi Electric Information Systems Corp | Electronic medical chart system |
| CN107562732A (en) * | 2017-10-26 | 2018-01-09 | 北京康夫子科技有限公司 | The processing method and system of electronic health record |
| CN108899064A (en) * | 2018-05-31 | 2018-11-27 | 平安医疗科技有限公司 | Electronic health record generation method, device, computer equipment and storage medium |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2216681A1 (en) * | 1996-09-30 | 1998-03-30 | Smithkline Beecham Corporation | Disease management method and system |
| US8579811B2 (en) * | 2006-09-19 | 2013-11-12 | 3M Innovative Properties Company | Medical diagnosis derived from patient drug history data |
| JP5504620B2 (en) * | 2008-12-10 | 2014-05-28 | 富士通株式会社 | Data selection support program, data selection support device, and data selection support method |
| WO2014186608A1 (en) * | 2013-05-15 | 2014-11-20 | Adverse Events, Inc. | System and method for surveillance and evaluation of safety risks associated with medical interventions |
| WO2014194118A2 (en) * | 2013-05-29 | 2014-12-04 | Revon Systems, Llc | Schedule-based electronic medical record modules, applications, and uses thereof |
| US20150356272A1 (en) * | 2014-06-10 | 2015-12-10 | Taipei Medical University | Prescription analysis system and method for applying probabilistic model based on medical big data |
| CN106803022A (en) * | 2017-01-25 | 2017-06-06 | 济南德健信息技术有限公司 | A kind of rational use of medicines monitoring method based on electronic health record |
| US20180308569A1 (en) * | 2017-04-25 | 2018-10-25 | S Eric Luellen | System or method for engaging patients, coordinating care, pharmacovigilance, analysis or maximizing safety or clinical outcomes |
| CN107341345A (en) * | 2017-06-22 | 2017-11-10 | 厦门大学 | A kind of clinical rational drug use methods of risk assessment based on electronic health record big data |
| CN107799160B (en) * | 2017-10-26 | 2021-12-31 | 医渡云（北京）技术有限公司 | Medication aid decision-making method and device, storage medium and electronic equipment |
| CN108630322B (en) * | 2018-04-27 | 2020-08-14 | 厦门大学 | Drug interaction modeling and risk assessment method, terminal device and storage medium |
-
2018
- 2018-12-28 CN CN201811624762.3A patent/CN111383726B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005316967A (en) * | 2004-03-31 | 2005-11-10 | Yuyama Manufacturing Co Ltd | Electronic medical chart device |
| JP2008217291A (en) * | 2007-03-02 | 2008-09-18 | Fujifilm Corp | Electronic medical record device and food instruction support method |
| JP2009146345A (en) * | 2007-12-18 | 2009-07-02 | Mitsubishi Electric Information Systems Corp | Electronic medical chart system |
| CN107562732A (en) * | 2017-10-26 | 2018-01-09 | 北京康夫子科技有限公司 | The processing method and system of electronic health record |
| CN108899064A (en) * | 2018-05-31 | 2018-11-27 | 平安医疗科技有限公司 | Electronic health record generation method, device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111383726A (en) | 2020-07-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11069432B2 (en) | Automatic disease detection from unstructured textual reports | |
| CN108920453B (en) | Data processing method and device, electronic equipment and computer readable medium | |
| CN111863267B (en) | Data information acquisition method, data analysis method, device and storage medium | |
| US10614196B2 (en) | System for automated analysis of clinical text for pharmacovigilance | |
| JP2019049964A (en) | Automatic identification and extraction of medical condition and fact from electronic medical treatment record | |
| US20220262527A1 (en) | Computer-implemented system and method for guided assessments on medication effects | |
| CN106068509B (en) | System and method for creating a longitudinal view of patient findings | |
| US10667794B2 (en) | Automatic detection of disease from analysis of echocardiographer findings in echocardiogram videos | |
| US11195600B2 (en) | Automatic discrepancy detection in medical data | |
| CN111383726B (en) | Electronic medical record data processing method and device, electronic equipment and readable medium | |
| CN114026651A (en) | Automatic generation of structured patient data records | |
| CN110504035A (en) | Medical data library and system | |
| US20210057058A1 (en) | Data processing method, apparatus, and device | |
| CN113243033A (en) | Integrated diagnostic system and method | |
| US20180107791A1 (en) | Cohort detection from multimodal data and machine learning | |
| CN111383761B (en) | Medical data analysis method, medical data analysis device, electronic equipment and computer readable medium | |
| CN112562808B (en) | Patient portrait generation method, apparatus, electronic device and storage medium | |
| Band et al. | A survey on machine learning and internet of medical things-based approaches for handling COVID-19: Meta-analysis | |
| Sindiramutty et al. | Explainable AI in Healthcare Application | |
| CN111863173B (en) | Medical record quality assessment method and computing equipment | |
| Huff et al. | Evaluation and verification of the global rapid identification of threats system for infectious diseases in textual data sources | |
| CN115775635A (en) | Medicine risk identification method and device based on deep learning model and terminal equipment | |
| CN114724693A (en) | Method and device for detecting abnormal diagnosis and treatment behaviors, electronic equipment and storage medium | |
| Machado et al. | Applying Business Intelligence to Clinical and Healthcare Organizations | |
| Zhao et al. | Depression Identification of Students Based on Campus Social Platform Data and Deep Learning |
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 |