CN112562808B - Patient portrait generation method, apparatus, electronic device and storage medium - Google Patents
Patient portrait generation method, apparatus, electronic device and storage medium Download PDFInfo
- Publication number
- CN112562808B CN112562808B CN202011460427.1A CN202011460427A CN112562808B CN 112562808 B CN112562808 B CN 112562808B CN 202011460427 A CN202011460427 A CN 202011460427A CN 112562808 B CN112562808 B CN 112562808B
- Authority
- CN
- China
- Prior art keywords
- patient
- event
- medical
- diagnosis
- treatment
- 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
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000003745 diagnosis Methods 0.000 claims abstract description 95
- 230000007721 medicinal effect Effects 0.000 claims abstract description 66
- 238000000605 extraction Methods 0.000 claims description 19
- 238000012545 processing Methods 0.000 claims description 16
- 238000010606 normalization Methods 0.000 claims description 2
- 238000003058 natural language processing Methods 0.000 abstract description 13
- 238000013473 artificial intelligence Methods 0.000 abstract description 11
- 238000013135 deep learning Methods 0.000 abstract description 6
- 230000019771 cognition Effects 0.000 abstract description 5
- 238000005516 engineering process Methods 0.000 description 14
- 238000004590 computer program Methods 0.000 description 10
- 238000004891 communication Methods 0.000 description 9
- 230000006399 behavior Effects 0.000 description 7
- 229940079593 drug Drugs 0.000 description 7
- 239000003814 drug Substances 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 201000010099 disease Diseases 0.000 description 5
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000003759 clinical diagnosis Methods 0.000 description 4
- 208000024891 symptom Diseases 0.000 description 4
- 206010002091 Anaesthesia Diseases 0.000 description 3
- 230000037005 anaesthesia Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000010882 preoperative diagnosis Methods 0.000 description 3
- 238000001356 surgical procedure Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 208000017667 Chronic Disease Diseases 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 238000003908 quality control method Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 230000001364 causal effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000003924 mental process Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000000554 physical therapy Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- G—PHYSICS
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The application discloses a method, a device, electronic equipment and a storage medium for generating a patient portrait, which relate to the technical field of computers, in particular to the technical field of artificial intelligence such as natural language processing, knowledge graph and deep learning. The specific implementation scheme is as follows: acquiring medical record information of a patient and acquiring medical activity information of the patient; extracting diagnosis and treatment events from medical record information and medical activity information; and generating a patient profile for the patient based on the extracted medical events. The generating method of the embodiment of the application can form more comprehensive, stereoscopic and complete cognition for the patient.
Description
Technical Field
The application relates to the technical field of computers, in particular to the technical field of artificial intelligence such as natural language processing, knowledge graph and deep learning, and especially relates to a method and device for generating a patient portrait, electronic equipment and a storage medium.
Background
Currently, EMR (Electronic Medical Record ) is a main carrier for recording various medical activities of patients in hospitals, and most of existing electronic medical record structuring technologies are directed to entities and relationships among entities, and entities in electronic medical records mainly include diseases, symptoms, medicines, inspection, examination, surgery and the like. Entity identification can also be performed on entities such as drugs and diseases.
Medical activity in EMR and relationships between medical activities are fundamental concepts for clinical informatics applications, such as auxiliary diagnostics, treatment effect analysis. The existing EMR only takes an original bill of a courtyard as a storage structure, and lacks clear and accurate semantic definition for medical activities; furthermore, many clinical events involve distribution among multiple documents, and orphaned EMR document structures are unable to express causal, compliant, chronological, etc., relationships that exist between different medical activities.
Disclosure of Invention
The application provides a patient portrait generation method, a device, an electronic device and a storage medium.
According to an aspect of the present application, there is provided a method for generating a patient portrait, including:
acquiring medical record information of a patient and acquiring medical activity information of the patient;
extracting a diagnosis and treatment event from the medical record information and the medical activity information; and
generating a patient representation of the patient from the extracted diagnostic events.
According to another aspect of the present application, there is provided a patient portrait generating apparatus, including:
the acquisition module is used for acquiring medical record information of a patient and acquiring medical activity information of the patient;
the extraction module is used for extracting diagnosis and treatment events from the medical record information and the medical activity information; and
and the generation module is used for generating a patient portrait of the patient according to the extracted diagnosis and treatment event.
According to another aspect of the present application, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of generating a patient representation of an embodiment of the above aspect.
According to another aspect of the present application, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method for generating a patient image according to the embodiment of the above aspect.
According to another aspect of the present application, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method for generating a patient representation according to the embodiment of the above aspect.
It should be understood that the description of this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
Drawings
The drawings are for better understanding of the present solution and do not constitute a limitation of the present application. Wherein:
FIG. 1 is a flow chart of a method for generating a patient representation according to an embodiment of the present application;
FIG. 2 is a flow chart of another method for generating a patient representation according to an embodiment of the present application;
FIG. 3 is a flow chart of another method for generating a patient representation according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a patient portrait generating device according to an embodiment of the present application; and
fig. 5 is a block diagram of an electronic device of a method of generating a patient representation according to an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The following describes a patient portrait generating method, apparatus, electronic device and storage medium according to embodiments of the present application with reference to the accompanying drawings.
Artificial intelligence is the discipline of studying certain mental processes and intelligent behaviors (e.g., learning, reasoning, thinking, planning, etc.) of a person using a computer, both in the technical field of hardware and in the technical field of software. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a deep learning technology, a big data processing technology, a knowledge graph technology and the like.
Natural language processing is an important direction in the fields of computer science and artificial intelligence. It is studying various theories and methods that enable effective communication between a person and a computer in natural language. Natural language processing is a science that integrates linguistics, computer science, and mathematics.
Deep learning is a new research direction in the field of machine learning. Deep learning is the inherent regularity and presentation hierarchy of learning sample data, and the information obtained during such learning is helpful in interpreting data such as text, images and sounds. Its final goal is to have the machine have analytical learning capabilities like a person, and to recognize text, image, and sound data. Deep learning is a complex machine learning algorithm that achieves far greater results in terms of speech and image recognition than prior art.
The method for generating the patient portrait provided in the embodiment of the present application may be executed by an electronic device, which may be a PC (Personal Computer ) computer, a tablet PC, a palm computer, or the like, and is not limited in any way.
In an embodiment of the application, the electronic device may be provided with a processing component, a storage component and a driving component. Alternatively, the driving component and the processing component may be integrally provided, and the storage component may store an operating system, an application program or other program modules, and the processing component implements the method for generating a patient portrait provided by the embodiments of the present application by executing the application program stored in the storage component.
Fig. 1 is a flow chart of a method for generating a patient portrait according to an embodiment of the present application.
The patient portrait generating method of the embodiment of the application can be further executed by the patient portrait generating device provided by the embodiment of the application, and the device can be configured in the electronic equipment to achieve the purposes of acquiring medical record information of a patient, acquiring medical activity information of the patient, extracting diagnosis and treatment events from the medical record information and the medical activity information, and generating the patient portrait of the patient according to the extracted diagnosis and treatment events, so that more comprehensive, more stereoscopic and more complete cognition of the patient can be formed.
As a possible case, the patient portrait generating method in the embodiment of the present application may also be executed at a server, where the server may be a cloud server, and the patient portrait generating method may be executed at a cloud.
As shown in FIG. 1, the method for generating a patient representation may include:
step 101, obtaining medical record information of a patient and obtaining medical activity information of the patient. Note that the medical record information described in this embodiment may be information described in the electronic medical record of the patient.
In embodiments of the present application, the information recorded in the electronic medical record may include one or more of disease information, symptom information, medication information, test information, examination information, current medical history, and the like. The medical activity information may include critical medical events for the patient's overall procedure, critical medical nodes, such as admission events, operational events, surgical events, consultation events, significant treatment change events, patient status return events, etc., i.e., a series of medical activities that develop around the patient's procedure during the patient's admission to discharge and are documented in a medical record. The medical activities can be classified into clinical diagnosis and treatment, clinical procedures, and patient conditions, wherein the clinical diagnosis and treatment can include medication, inspection, examination, surgery, etc., the clinical procedures can include admission, discharge, transfer, etc., and the patient conditions can include critical events, death events, etc.
It should be noted that, the electronic medical record described in this embodiment may be stored in a storage space (e.g., a computer) of an electronic device of a related person (e.g., a medical care person), where the storage space is not limited to an entity-based storage space, for example, a hard disk, and the storage space may also be a storage space (cloud storage space) of a server (e.g., a server of a hospital) connected to the electronic device; the medical activity information described in this embodiment may also be stored in the memory space of the electronic device of the person concerned. For example, a doctor may store relevant inquiry information in a computer to form an electronic medical record (i.e., medical record information) during an inquiry of a patient; the medical activity information can be stored in the computer by relevant personnel in the hospital during the treatment of the patient.
In particular, the relevant person can call out medical record information and medical activity information of the patient through an electronic device (e.g., a computer).
Step 102, a diagnosis and treatment event is extracted from the medical record information and the medical activity information.
In embodiments of the present application, a diagnosis and treatment event may be used to describe a medical behavior received by a patient during a whole course of a visit, where the diagnosis and treatment event may include an event type, such as a surgical event, a consultation event, a ward-round event, etc.; event arguments, e.g., arguments of surgical events, are: surgical item, surgical time, surgical code, surgeon, surgical grade, surgical assistant, anesthesiologist, anesthesia modality, preoperative diagnosis.
It should be noted that, the hospital may form a diagnosis and treatment event knowledge system based on the diagnosis and treatment event described in the embodiment, where the diagnosis and treatment event knowledge system may be defined by combining the latest concepts of the industry on the diagnosis and treatment event, and is constructed by developing, medical specialists, and product three parties together; furthermore, according to the diagnosis and treatment event extraction requirement of medical records quality control, event type discrimination and related event argument extraction are carried out from one or more documents, and the diagnosis and treatment activity progress of the whole patient treatment process is displayed according to event granularity, so that the medical behavior can be truly reflected.
In embodiments of the present application, medical events may be extracted from medical record information and medical activity information based on natural language processing techniques and medical knowledge maps.
And 103, generating a patient portrait of the patient according to the extracted diagnosis and treatment event.
The patient portrait described in this embodiment may be a large amount of data such as electronic medical records and physical examination reports generated during the collection of individual crowd information of the patient, clinical treatment, periodic physical examination, chronic disease monitoring, and the like. As will be readily appreciated, patient portrayal is the application of a "tag" to a patient, and a tag is typically a highly identifiable identifier, such as one that identifies the patient in multiple dimensions, such as by age, gender, region, preference, family history, past illness history, etc.
Specifically, the electronic device may extract a diagnosis and treatment event from the medical record information and the medical activity information by using a natural language processing technology and a medical knowledge graph after retrieving the medical record information and the medical activity information of the patient, and then generate a patient portrait of the patient according to the extracted diagnosis and treatment event.
It should be noted that the medical knowledge graph described in this embodiment may be stored in the storage space of the electronic device in advance, so as to be retrieved for use when needed.
In the embodiment of the application, firstly, medical record information of a patient is acquired, medical activity information of the patient is acquired, then a diagnosis and treatment event is extracted from the medical record information and the medical activity information, and finally, a patient portrait of the patient is generated according to the extracted diagnosis and treatment event, so that more comprehensive, three-dimensional and complete cognition of the patient can be formed, and application of clinical AI (Artificial Intelligence ) technology is facilitated.
To clearly illustrate the above embodiment, in one embodiment of the present application, as shown in fig. 2, extracting a diagnosis and treatment event from medical record information and medical activity information may include:
step 201, extracting a plurality of entities from medical record information and medical activity information.
It should be noted that the entities described in this embodiment may include diseases, symptoms, medicines, tests, examinations, operations, consultations, and the like.
In embodiments of the present application, multiple entities may be extracted from medical record information and medical activity information based on natural language processing techniques.
It should be noted that, in other embodiments of the present application, multiple entities may be extracted from medical record information and medical activity information based on natural language processing techniques and medical knowledge-graph.
As a possible scenario, in the embodiment of the present application, a physical extraction model may be trained using a natural language processing technique, a medical knowledge graph, and a neural network, or a natural language processing technique and a neural network, for performing the extraction of the plurality of entities.
Specifically, the electronic device may input medical record information and medical activity information to the entity extraction model after retrieving the medical record information and medical activity information of the patient, so that the medical record information and the medical activity information are extracted through the entity extraction model, so that the entity extraction model outputs a plurality of entities.
It should be noted that, the entity extraction model described in this embodiment may be trained in advance and pre-stored in a storage space of the electronic device, so as to facilitate the retrieval application.
Step 202, generating a plurality of diagnosis and treatment events according to the medical knowledge graph and a plurality of entities, and extracting event arguments and event types of each diagnosis and treatment event.
In embodiments of the present application, the event types of the diagnosis event may include, for example, a surgical event type, a consultation event type, a physiotherapy event type, etc., and the event argument of the diagnosis event may be arranged for the specific operation level of the event, for example, the argument of the surgical event is: the arguments of surgical items, surgical time, surgical code, surgeon, surgical grade, surgical assistant, anesthesiologist, anesthesia modality, preoperative diagnosis, etc., and consultation events may be: consultation time, consultation scheme, consultation physician, etc.
Specifically, the electronic device may generate a plurality of diagnosis and treatment events according to the medical knowledge graph and the plurality of entities after extracting the plurality of entities, and extract an event argument and an event type of each diagnosis and treatment event. Thus, more abundant information can be acquired, and diversified relationships between medical activities can be reflected more deeply.
Further, in an embodiment of the present application, the method for generating a patient profile may further include inputting medical record information and medical activity information into a medical event inference model, wherein the medical event inference model generates event relationships between a plurality of diagnosis and treatment events according to a medical knowledge graph.
It should be noted that, the medical event inference model described in this embodiment may be trained in advance and pre-stored in the storage space of the electronic device, so as to facilitate the retrieval application.
Specifically, after retrieving medical record information and medical activity information of a patient, the electronic device may input the medical record information and the medical activity information into a medical event inference model, so that the medical record information and the medical activity information are processed according to a medical knowledge graph through the medical event inference model to output event relationships among a plurality of related diagnosis and treatment events. Therefore, the medical behaviors among different documents can be analyzed through the event relations among a plurality of diagnosis and treatment events, the medical behaviors of the different documents are subjected to association analysis through the event relations, the medical records are fully understood in multiple granularity and multiple layers, and the control of the quality of the medical records in all aspects can be realized.
In one embodiment of the present application, as shown in FIG. 3, generating a patient representation of a patient from the extracted medical events may include:
step 301, extracting event occurrence time from event arguments of a plurality of diagnosis and treatment events. For example, the surgical time may be extracted from an argument of a surgical event, the consultation time may be extracted from an argument of a consultation event, and so on.
Step 302, sorting the plurality of diagnosis and treat events according to the time of the plurality of diagnosis and treat events according to the time sequence, and displaying the event relation among the plurality of diagnosis and treat events in real time in the patient portrait, and respectively corresponding event argument and event type of the diagnosis and treat event.
Specifically, the electronic device may extract event occurrence time from among event arguments of a plurality of diagnosis events, for example, extract operation time from arguments of an operation event, extract consultation time from arguments of a consultation event, etc., after acquiring event relationships among the plurality of diagnosis events. And then sequencing a plurality of diagnosis and treatment events according to the time of the plurality of diagnosis and treatment events according to the time sequence, displaying the event relation among the plurality of diagnosis and treatment events in real time and the event theory elements and event types respectively corresponding to the diagnosis and treatment events in the patient portrait, so that the patient portrait can conveniently carry out complex inquiry of clinical research in the form of the diagnosis and treatment events, such as auxiliary diagnosis and treatment effect analysis, reorganizes all the diagnosis and treatment events in the patient diagnosis process through the time sequence relation, the compliance relation, the causality relation and the like of the diagnosis and treatment events, and provides a higher-level and larger-granularity patient portrait view for the outside in a multi-dimensional three-dimensional structure form. Further, a comprehensive, accurate and multidimensional patient portrayal system is constructed for the hospital.
Further, in an embodiment of the present application, the method for generating a patient portrait may further include normalizing the entity among the plurality of diagnosis and treatment events.
In the embodiment of the application, the normalization processing is performed on the entities among the multiple diagnosis and treatment events, so that the entity data among the multiple diagnosis and treatment events is limited in a certain range (such as [0,1] or [ -1,1 ]), thereby eliminating the adverse effect caused by the singular data. And in the process of generating the patient portrait, the entity among a plurality of diagnosis and treatment events is normalized, so that the convergence speed and the precision of the related model are improved.
Fig. 4 is a schematic structural diagram of a patient portrait generating device according to an embodiment of the present application.
The patient portrait generating device can be configured in the electronic equipment to acquire medical record information of a patient, acquire medical activity information of the patient, extract diagnosis and treatment events from the medical record information and the medical activity information, and generate patient portraits of the patient according to the extracted diagnosis and treatment events, so that more comprehensive, three-dimensional and complete cognition of the patient can be formed.
As shown in fig. 4, the patient portrait generating apparatus 400 may include: an acquisition module 410, an extraction module 420, and a generation module 430.
The acquiring module 410 is configured to acquire medical record information of a patient, and acquire medical activity information of the patient. Note that the medical record information described in this embodiment may be information described in the electronic medical record of the patient.
In embodiments of the present application, the information recorded in the electronic medical record may include one or more of disease information, symptom information, medication information, test information, examination information, current medical history, and the like. The medical activity information may include critical medical events for the patient's overall procedure, critical medical nodes, such as admission events, operational events, surgical events, consultation events, significant treatment change events, patient status return events, etc., i.e., a series of medical activities that develop around the patient's procedure during the patient's admission to discharge and are documented in a medical record. The medical activities can be classified into clinical diagnosis and treatment, clinical procedures, and patient conditions, wherein the clinical diagnosis and treatment can include medication, inspection, examination, surgery, etc., the clinical procedures can include admission, discharge, transfer, etc., and the patient conditions can include critical events, death events, etc.
It should be noted that, the electronic medical record described in this embodiment may be stored in a storage space (e.g., a computer) of an electronic device of a related person (e.g., a medical care person), where the storage space is not limited to an entity-based storage space, for example, a hard disk, and the storage space may also be a storage space (cloud storage space) of a server (e.g., a server of a hospital) connected to the electronic device; the medical activity information described in this embodiment may also be stored in the memory space of the electronic device of the person concerned. For example, a doctor may store relevant inquiry information in a computer to form an electronic medical record (i.e., medical record information) during an inquiry of a patient; the medical activity information can be stored in the computer by relevant personnel in the hospital during the treatment of the patient.
In particular, the acquisition module 410 can recall medical record information and medical activity information of a patient via an electronic device (e.g., a computer).
The extraction module 420 is used for extracting diagnosis and treatment events from medical record information and medical activity information.
In embodiments of the present application, a diagnosis and treatment event may be used to describe a medical behavior received by a patient during a whole course of a visit, where the diagnosis and treatment event may include an event type, such as a surgical event, a consultation event, a ward-round event, etc.; event arguments, e.g., arguments of surgical events, are: surgical item, surgical time, surgical code, surgeon, surgical grade, surgical assistant, anesthesiologist, anesthesia modality, preoperative diagnosis.
It should be noted that, the hospital may form a diagnosis and treatment event knowledge system based on the diagnosis and treatment event described in the embodiment, where the diagnosis and treatment event knowledge system may be defined by combining the latest concepts of the industry on the diagnosis and treatment event, and is constructed by developing, medical specialists, and product three parties together; furthermore, according to the diagnosis and treatment event extraction requirement of medical records quality control, event type discrimination and related event argument extraction are carried out from one or more documents, and the diagnosis and treatment activity progress of the whole patient treatment process is displayed according to event granularity, so that the medical behavior can be truly reflected.
In embodiments of the present application, the extraction module 420 may extract medical events from medical record information and medical activity information based on natural language processing techniques and medical knowledge maps.
The generation module 430 is configured to generate a patient representation of the patient based on the extracted medical events.
The patient portrait described in this embodiment may be a large amount of data such as electronic medical records and physical examination reports generated during the collection of individual crowd information of the patient, clinical treatment, periodic physical examination, chronic disease monitoring, and the like. As will be readily appreciated, patient portrayal is the application of a "tag" to a patient, and a tag is typically a highly identifiable identifier, such as one that identifies the patient in multiple dimensions, such as by age, gender, region, preference, family history, past illness history, etc.
Specifically, the acquisition module 410 may extract medical events from the medical record information and the medical activity information using natural language processing technology and a medical knowledge graph after retrieving the medical record information and the medical activity information of the patient, and then the generation module 430 generates a patient portrait of the patient according to the extracted medical events.
It should be noted that the medical knowledge graph described in this embodiment may be stored in the storage space of the electronic device in advance, so as to be retrieved for use when needed.
In the embodiment of the application, the medical record information of the patient and the medical activity information of the patient are acquired through the acquisition module, the diagnosis and treatment event is extracted from the medical record information and the medical activity information through the extraction module, and the patient portrait of the patient is generated through the generation module according to the extracted diagnosis and treatment event, so that more comprehensive, three-dimensional and complete cognition of the patient can be formed, and the application of clinical AI technology is facilitated.
In one embodiment of the present application, the extraction module 420 is specifically configured to extract a plurality of entities from medical record information and medical activity information, generate a plurality of diagnosis and treatment events according to a medical knowledge graph and the plurality of entities, and extract an event argument and an event type of each diagnosis and treatment event.
In one embodiment of the present application, as shown in fig. 4, the patient profile generation apparatus 400 may further include an input module 440, wherein the input module 440 is configured to input medical record information and medical activity information into a medical event reasoning model, wherein the medical event reasoning model generates event relationships between a plurality of medical events according to a medical knowledge graph.
In one embodiment of the present application, the generating module 430 is specifically configured to extract event occurrence time from event arguments of multiple diagnosis and treatment events, sort the multiple diagnosis and treatment events according to time of the multiple diagnosis and treatment events according to time sequence, and display event relationships among the multiple diagnosis and treatment events in real time in the patient profile, and event arguments and event types corresponding to the diagnosis and treatment events respectively.
In one embodiment of the present application, as shown in fig. 4, the patient portrait generating apparatus 400 may further include a processing module 450, where the processing module 450 is configured to normalize entities among a plurality of diagnosis and treatment events.
The explanation of the embodiment of the method for generating a patient image is also applicable to the apparatus for generating a patient image of this embodiment, and will not be repeated here.
According to the patient portrait generation device, medical record information of a patient and medical activity information of the patient are acquired through the acquisition module, diagnosis and treatment events are extracted from the medical record information and the medical activity information through the extraction module, and the patient portrait of the patient is generated according to the extracted diagnosis and treatment events through the generation module. Thus, a more comprehensive, stereoscopic and complete knowledge of the patient can be formed, and the application of clinical AI technology is facilitated.
According to embodiments of the present application, there is also provided an electronic device, a readable storage medium and a computer program product.
Fig. 5 shows a schematic block diagram of an example electronic device 500 that may be used to implement embodiments of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the application described and/or claimed herein.
As shown in fig. 5, the apparatus 500 includes a computing unit 501 that can perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 505 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, ROM 502, and RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Various components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, etc.; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 505 such as a magnetic disk, an optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the generation of patient portraits. For example, in some embodiments, the method of generating a patient representation may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 505. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into RAM 503 and executed by computing unit 501, one or more steps of the patient representation generation method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the method of generating a patient representation in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet, internets, and blockchain networks.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions disclosed in the present application can be achieved, and are not limited herein.
The above embodiments do not limit the scope of the application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application are intended to be included within the scope of the present application.
Claims (8)
1. A method of generating a patient representation, comprising:
acquiring medical record information of a patient and acquiring medical activity information of the patient;
extracting a diagnosis and treatment event from the medical record information and the medical activity information; and
generating a patient representation of the patient from the extracted diagnostic event;
the extracting a diagnosis and treatment event from the medical record information and the medical activity information comprises:
extracting a plurality of entities from the medical record information and the medical activity information;
generating a plurality of diagnosis and treatment events according to the medical knowledge graph and the entities, and extracting event arguments and event types of each diagnosis and treatment event;
the generating a patient representation of the patient from the extracted diagnostic event comprises:
extracting event occurrence time from event arguments of the plurality of diagnosis and treatment events;
according to the time sequence, sorting the diagnosis and treatment events according to the time of the diagnosis and treatment events, and displaying event relations among the diagnosis and treatment events in real time in the patient portrait, and event theory elements and event types respectively corresponding to the diagnosis and treatment events.
2. The patient representation generation method of claim 1, further comprising:
and inputting the medical record information and the medical activity information into a medical event reasoning model, wherein the medical event reasoning model generates event relations among the plurality of diagnosis and treatment events according to the medical knowledge graph.
3. The patient representation generation method of claim 1, further comprising:
and normalizing the entities in the plurality of diagnosis and treatment events.
4. A patient representation generating apparatus comprising:
the acquisition module is used for acquiring medical record information of a patient and acquiring medical activity information of the patient;
the extraction module is used for extracting diagnosis and treatment events from the medical record information and the medical activity information; and
a generation module for generating a patient representation of the patient from the extracted diagnosis and treatment event; the extraction module is specifically configured to:
extracting a plurality of entities from the medical record information and the medical activity information;
generating a plurality of diagnosis and treatment events according to the medical knowledge graph and the entities, and extracting event arguments and event types of each diagnosis and treatment event;
the generating module is specifically configured to:
extracting event occurrence time from event arguments of the plurality of diagnosis and treatment events;
according to the time sequence, sorting the diagnosis and treatment events according to the time of the diagnosis and treatment events, and displaying event relations among the diagnosis and treatment events in real time in the patient portrait, and event theory elements and event types respectively corresponding to the diagnosis and treatment events.
5. The patient representation generating apparatus of claim 4, further comprising:
the input module is used for inputting the medical record information and the medical activity information into a medical event reasoning model, wherein the medical event reasoning model generates event relations among the plurality of diagnosis and treatment events according to the medical knowledge graph.
6. The patient representation generating apparatus of claim 4, further comprising:
and the processing module is used for carrying out normalization processing on the entities in the diagnosis and treatment events.
7. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of generating a patient representation of any one of claims 1-3.
8. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of generating a patient representation according to any one of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011460427.1A CN112562808B (en) | 2020-12-11 | 2020-12-11 | Patient portrait generation method, apparatus, electronic device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011460427.1A CN112562808B (en) | 2020-12-11 | 2020-12-11 | Patient portrait generation method, apparatus, electronic device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112562808A CN112562808A (en) | 2021-03-26 |
| CN112562808B true CN112562808B (en) | 2024-01-19 |
Family
ID=75062582
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011460427.1A Active CN112562808B (en) | 2020-12-11 | 2020-12-11 | Patient portrait generation method, apparatus, electronic device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112562808B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112992362B (en) * | 2021-04-19 | 2021-09-14 | 北京妙医佳健康科技集团有限公司 | Body state evaluation device, electronic equipment and storage medium |
| CN113590775B (en) * | 2021-06-22 | 2023-07-21 | 北京百度网讯科技有限公司 | Diagnosis and treatment data processing method and device, electronic equipment and storage medium |
| CN115098622A (en) * | 2022-05-30 | 2022-09-23 | 医渡云（北京）技术有限公司 | Medical event identification method, device, equipment and storage medium |
| CN117116407B (en) * | 2023-10-23 | 2024-02-23 | 之江实验室 | Patient portrait generating method, device and storage medium based on electronic medical record |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106529177A (en) * | 2016-11-12 | 2017-03-22 | 杭州电子科技大学 | Patient portrait drawing method and device based on medical big data |
| CN109524066A (en) * | 2018-11-09 | 2019-03-26 | 医渡云（北京）技术有限公司 | Medical data processing method and processing device, storage medium and electronic equipment |
| CN109902112A (en) * | 2019-01-24 | 2019-06-18 | 西安交通大学 | A kind of electronic health record method for visualizing and visualization system based on time shaft |
| CN110955764A (en) * | 2019-11-19 | 2020-04-03 | 百度在线网络技术（北京）有限公司 | Scene knowledge graph generation method, man-machine conversation method and related equipment |
| CN111506734A (en) * | 2019-01-30 | 2020-08-07 | 国家计算机网络与信息安全管理中心 | Event evolution knowledge graph construction method, device, equipment and storage medium |
| CN111967256A (en) * | 2020-06-30 | 2020-11-20 | 北京百度网讯科技有限公司 | Event relation generation method and device, electronic equipment and storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005122042A2 (en) * | 2004-06-02 | 2005-12-22 | Catalis, Inc. | Method and system for generating medical narrative |
| US20140297317A1 (en) * | 2013-03-27 | 2014-10-02 | International Business Machines Corporation | Extracting key action patterns from patient event data |
-
2020
- 2020-12-11 CN CN202011460427.1A patent/CN112562808B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106529177A (en) * | 2016-11-12 | 2017-03-22 | 杭州电子科技大学 | Patient portrait drawing method and device based on medical big data |
| CN109524066A (en) * | 2018-11-09 | 2019-03-26 | 医渡云（北京）技术有限公司 | Medical data processing method and processing device, storage medium and electronic equipment |
| CN109902112A (en) * | 2019-01-24 | 2019-06-18 | 西安交通大学 | A kind of electronic health record method for visualizing and visualization system based on time shaft |
| CN111506734A (en) * | 2019-01-30 | 2020-08-07 | 国家计算机网络与信息安全管理中心 | Event evolution knowledge graph construction method, device, equipment and storage medium |
| CN110955764A (en) * | 2019-11-19 | 2020-04-03 | 百度在线网络技术（北京）有限公司 | Scene knowledge graph generation method, man-machine conversation method and related equipment |
| CN111967256A (en) * | 2020-06-30 | 2020-11-20 | 北京百度网讯科技有限公司 | Event relation generation method and device, electronic equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| 基于互联网大数据的事件智能抓取和画像;李方方;陈容华;秦胜男;戴胜;;无线互联科技(01);全文 * |
| 基于大数据平台的企业画像研究综述;田娟;朱定局;杨文翰;;计算机科学(S2);全文 * |
| 基于大数据的患者画像标签体系构建方法及应用研究;姚华彦;张鑫金;何萍;;中国卫生信息管理杂志(06);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112562808A (en) | 2021-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112562808B (en) | Patient portrait generation method, apparatus, electronic device and storage medium | |
| Awan et al. | Machine learning in heart failure: ready for prime time | |
| US20200303072A1 (en) | Method and system for supporting medical decision making | |
| RU2686627C1 (en) | Automatic development of a longitudinal indicator-oriented area for viewing patient's parameters | |
| CN113345577B (en) | Diagnosis and treatment auxiliary information generation method, model training method, device, equipment and storage medium | |
| CN116383413B (en) | Knowledge graph updating method and system based on medical data extraction | |
| Pesquita | Towards Semantic Integration for Explainable Artificial Intelligence in the Biomedical Domain. | |
| Sethi et al. | Use of artificial intelligence in healthcare systems: state-of-the-art survey | |
| Omotunde et al. | The Modern Impact of Artificial Intelligence Systems in Healthcare: A Concise Analysis | |
| Roham et al. | A systematic review of knowledge visualization approaches using big data methodology for clinical decision support | |
| Pandit et al. | Artificial neural networks in healthcare: A systematic review | |
| Hussain | Artificial intelligence in healthcare explained for computer-non-experts | |
| Iqbal et al. | AI technologies in health-care applications | |
| Bala et al. | Applications of Machine Learning and Deep Learning for maintaining Electronic Health Records | |
| Georgieva-Tsaneva | Effective information methods for description and storage of data in health care | |
| US11928121B2 (en) | Scalable visual analytics pipeline for large datasets | |
| Hooda et al. | Semantic driven healthcare monitoring and disease detection framework from heterogeneous sensor data | |
| Dey et al. | U-Healthcare Monitoring Systems: Volume 1: Design and Applications | |
| Haroon et al. | Improving the healthcare and public health critical infrastructure by soft computing: An overview | |
| Shah et al. | Recent Challenges in Medical Science using Machine Learning Techniques: A Review | |
| Priya et al. | Machine and Deep Learning Classifications for IoT-Enabled Healthcare Devices | |
| Belgacem et al. | Machine Learning in the Medical Field: A Comprehensive Overview | |
| Kale et al. | Heterogeneous data management in IoT-based health care systems | |
| Sassi et al. | The connected electronic health record: a semantic-enabled, flexible, and unified electronic health record | |
| US20240156415A1 (en) | Diagnosis support system and diagnosis support apparatus |
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 |