CN112562808A - Patient portrait generation method and device, electronic equipment and storage medium - Google Patents
Patient portrait generation method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application discloses a patient portrait generation method and device, electronic equipment and a storage medium, and relates to the technical field of computers, in particular to the technical fields of artificial intelligence such as natural language processing, knowledge maps 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 representation of the patient from the extracted clinical events. The generation method provided by the embodiment of the application can form more comprehensive, more three-dimensional and more complete cognition on 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 maps and deep learning, and particularly relates to a patient portrait generation method and device, 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 the existing Electronic Medical Record structuring technologies are directed at entities and relationships between entities, wherein the entities in the Electronic Medical Record mainly include diseases, symptoms, medicines, examinations, surgeries, and the like. Entity identification can also be extracted for entities such as drugs, diseases, etc.
Medical activities in EMRs and relationships between medical activities are fundamental concepts for clinical informatics applications, such as assisted diagnosis, treatment effect analysis. The existing EMR only takes the original documents of academies as a storage structure, and lacks clear and accurate semantic definition of medical activities; further, many clinical events involve distribution among multiple documents, and the isolated EMR document structure fails to express causal, sequential, chronological relationships that exist between different medical activities.
Disclosure of Invention
The application provides a method and a device for generating a patient portrait, an electronic device and a storage medium.
According to an aspect of the present application, there is provided 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 clinical event.
According to another aspect of the present application, there is provided a patient representation generation 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 generating module for generating a patient representation 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 content of the first and second substances,
the memory stores instructions executable by the at least one processor to cause the at least one processor to perform a method of generating a patient representation as described in an embodiment of an aspect above.
According to another aspect of the present application, there is provided a non-transitory computer readable storage medium having stored thereon a computer program for causing a computer to execute a method of generating a patient representation according to an 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 a method of generating a patient representation as described in an embodiment of the above aspect.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a flow chart illustrating a method for generating a patient representation according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart illustrating another method for generating a patient representation according to an embodiment of the present disclosure;
FIG. 3 is a flow chart illustrating another method for generating a patient representation according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a patient image generating apparatus according to an embodiment of the present disclosure; 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
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those 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.
A patient representation generation method, apparatus, electronic device, and storage medium according to an embodiment of the present application are described below with reference to the drawings.
Artificial intelligence is the subject of research on the use of computers to simulate certain mental processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.) of humans, both in the hardware and software domain. 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, deep learning, a big data processing technology, a knowledge map technology and the like.
Natural language processing is an important direction in the fields of computer science and artificial intelligence. It studies various theories and methods that enable efficient communication between humans and computers using natural language. Natural language processing is a science integrating linguistics, computer science and mathematics.
Deep learning is a new research direction in the field of machine learning. Deep learning is the intrinsic law and expression level of the learning sample data, and the information obtained in the learning process is very helpful for the interpretation of data such as characters, images and sounds. The final aim of the method is to enable the machine to have the analysis and learning capability like a human, and to recognize data such as characters, images and sounds. Deep learning is a complex machine learning algorithm, and achieves the effect in speech and image recognition far exceeding the prior related art.
The method for generating a patient image according to the embodiment of the present application may be executed by an electronic device, where the electronic device may be a Personal Computer (PC), a tablet Computer, a palmtop Computer, or the like, and is not limited herein.
In the embodiment of the application, the electronic device can be provided with a processing component, a storage component and a driving component. Alternatively, the driving component and the processing component may be integrated, the storage component may store an operating system, an application program or other program modules, and the processing component implements the patient representation generation method provided by the embodiment of the present application by executing the application program stored in the storage component.
Fig. 1 is a flowchart illustrating a method for generating a patient image according to an embodiment of the present disclosure.
The patient portrait generation method provided by the embodiment of the application can be further executed by the patient portrait generation device provided by the embodiment of the application, and the device can be configured in 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 the patient portrait of the patient according to the extracted diagnosis and treatment events, so that more comprehensive, more three-dimensional and more complete cognition on the patient can be formed.
As a possible situation, the method for generating a patient representation according to the embodiment of the present application may also be executed at a server, where the server may be a cloud server, and the method for generating a patient representation may be executed at a cloud end.
As shown in FIG. 1, the method for generating a patient representation may include:
In the embodiment of the present application, the information recorded in the electronic medical record may include one or more of disease information, symptom information, drug information, test information, examination information, current medical history, and the like. The medical activity information can include key medical events, key medical nodes, such as admission events, operation events, consultation events, major treatment change events, patient status relegation events, and the like of the whole process of patient treatment, namely, a series of medical activities which are developed around the process of patient treatment and recorded on a medical record during the period from patient admission to discharge. 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, examination, operation and the like, the clinical procedures can include admission, discharge, transfer and the like, and the patient conditions can include critical events, serious events, death events and the like.
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 relevant person (e.g., a medical care provider), wherein the storage space is not limited to a physical-based storage space such as 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 physician can store relevant interrogation information in a computer to form an electronic medical record (i.e., medical record information) during an interrogation of a patient; the hospital personnel can store the medical activity information in a computer during the treatment of the patient.
Specifically, the relevant person may call up medical record information and medical activity information of the patient through an electronic device (e.g., a computer).
And 102, extracting diagnosis and treatment events from the medical record information and the medical activity information.
In the embodiment of the present application, the diagnosis and treatment event may be used to describe the medical behavior received by the patient during the whole diagnosis process, wherein the diagnosis and treatment event may include event types, such as a surgical event, a consultation event, a ward round event, and the like; event arguments, for example, for surgical events, are: surgical items, surgical time, surgical code, surgeon, surgical grade, surgical assistant, anesthesiologist, anesthesia modality, pre-operative diagnosis.
It should be noted that a hospital can form a diagnosis and treatment event knowledge system based on the diagnosis and treatment events described in this embodiment, wherein the diagnosis and treatment event knowledge system can be jointly constructed by three parties, i.e., research and development, medical experts and products, in combination with the latest concept definition of the industry on the diagnosis and treatment events; moreover, according to the diagnosis and treatment event extraction requirement of the quality control of the medical records, the event type judgment and the related event argument extraction are carried out from one or more documents, the diagnosis and treatment activity progress of the whole process of the patient in the treatment is displayed by the event granularity, and the medical behavior can be truly reflected.
In an embodiment of the present application, medical events may be extracted from medical record information and medical activity information based on natural language processing techniques and a medical knowledge graph.
It should be noted that the patient image described in this embodiment may be data collected such as individual population information of the patient, and a large number of electronic medical records, physical examination reports, etc. generated during clinical treatment, periodic physical examination, chronic disease monitoring, etc. As will be readily appreciated, a patient image is "tagged" to a patient, and a tag is typically a highly identifiable identifier that identifies the patient in a number of dimensions, such as age, gender, region, preferences, family history, past medical history, and the like.
Specifically, after calling medical record information and medical activity information of a patient, the electronic device can 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, and then generate a patient image of the patient according to the extracted diagnosis and treatment event.
It should be noted that the medical knowledge map described in this embodiment may be stored in a storage space of the electronic device in advance, so as to be called for use when necessary.
In the embodiment of the application, medical record information of a patient is firstly acquired, medical activity information of the patient is acquired, a diagnosis and treatment event is then extracted from the medical record information and the medical activity information, and a patient portrait of the patient is finally generated according to the extracted diagnosis and treatment event, so that more comprehensive, more three-dimensional and more complete cognition on the patient can be formed, and the application of an Artificial Intelligence (AI) technology is facilitated.
To clearly illustrate the above embodiment, in an embodiment of the present application, as shown in fig. 2, extracting the diagnosis and treatment event from the medical record information and the medical activity information may include:
in step 201, a plurality of entities are extracted from medical record information and medical activity information.
It is noted that the entities described in this embodiment may include diseases, symptoms, drugs, tests, examinations, surgeries, and consultation, etc.
In an embodiment of the application, a plurality of entities may be extracted from medical record information and medical activity information based on natural language processing techniques.
It is 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 maps.
As a possible scenario, in the embodiment of the present application, an entity extraction model may be trained using natural language processing technology, medical knowledge graph and neural network, or natural language processing technology and neural network, for performing the above-mentioned extraction of multiple entities.
Specifically, after calling out the medical record information and the medical activity information of the patient, the electronic device can input the medical record information and the medical activity information into the entity extraction model, so that the medical record information and the medical activity information are extracted through the entity extraction model, and 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 the storage space of the electronic device to facilitate the retrieval of the application.
In the embodiment of the present application, the event type of the diagnosis and treatment event may include, for example, a surgical event type, a consultation event type, a physiotherapy event type, and the like, and the event argument of the diagnosis and treatment event may be arranged for the specific operation level of the event, for example, the argument of the surgical event includes: the arguments for the surgical project, surgical time, surgical code, surgeon, surgical grade, surgical assistant, anesthesiologist, anesthesia modality, pre-operative diagnosis, etc., and consultation events may be: consultation time, consultation protocol, consultation physician, etc.
Specifically, the electronic device, after extracting the plurality of entities, may generate a plurality of clinical events from the medical knowledge graph and the plurality of entities, and extract event arguments and event types for each of the clinical events. Thus, more abundant information can be acquired, and thus diversified relationships between medical activities can be reflected more deeply.
Further, in an embodiment of the present application, the method for generating a patient representation 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 medical events according to the 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 memory space of the electronic device to facilitate retrieval of the application.
Specifically, after calling out medical record information and medical activity information of a patient, the electronic device can input the medical record information and the medical activity information into the medical event inference model, so that the medical record information and the medical activity information are processed by the medical event inference model according to the medical knowledge graph to output event relationships among a plurality of related diagnosis and treatment events. Therefore, medical behaviors among different documents can be analyzed through event relations among a plurality of diagnosis and treatment events, the medical behaviors of different documents are subjected to correlation analysis through the event relations, the medical records are fully understood in multiple granularities and multiple levels, and the comprehensive quality control of the medical records can be realized.
In one embodiment of the present application, as shown in fig. 3, generating a patient representation of a patient from extracted clinical 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 the surgical event, the consultation time may be extracted from an argument of the consultation event, and so forth.
Step 302, sequencing the plurality of diagnosis and treatment events according to the time of the plurality of diagnosis and treatment events according to the time sequence, and displaying the real-time event relationship among the plurality of diagnosis and treatment events, and the event argument and the event type corresponding to the diagnosis and treatment events respectively in the patient portrait.
Specifically, after acquiring the event relationship between the plurality of clinical events, the electronic device may extract the event occurrence time from event arguments of the plurality of clinical events, for example, extract the operation time from the argument of the operation event, extract the consultation time from the argument of the consultation event, and the like. Then, according to the time sequence, sequencing the plurality of diagnosis and treatment events according to the time of the plurality of diagnosis and treatment events, and displaying the real-time event relationship among the plurality of diagnosis and treatment events, and the event arguments and the event types corresponding to the diagnosis and treatment events respectively in the patient portrait, therefore, the patient portrait can conveniently carry out complex query of clinical research in the form of the diagnosis and treatment events, such as auxiliary diagnosis and treatment effect analysis, reorganizes all diagnosis and treatment events in the patient treatment process through diagnosis and treatment event time sequence relationship, sequential bearing relationship, causal relationship and the like, and provides a higher-level and larger-granularity patient portrait view externally in a multi-dimensional three-dimensional structure form. Furthermore, a comprehensive, accurate and multidimensional patient portrait system is established for the hospital.
Further, in an embodiment of the present application, the method for generating a patient representation may further include normalizing an entity in the plurality of clinical events.
In the embodiment of the application, the entity data in the plurality of clinical events can be limited within a certain range (such as [0,1] or [ -1,1]) by performing normalization processing on the entity in the plurality of clinical events, so that adverse effects caused by singular data can be eliminated. And the entity in a plurality of diagnosis and treatment events is normalized in the process of generating the patient portrait, which is beneficial to improving the convergence speed and precision of the correlation model.
Fig. 4 is a schematic structural diagram of a patient image generating apparatus according to an embodiment of the present application.
The generation device of drawing a portrait of patient of this application embodiment can be configured in electronic equipment to realize acquireing patient's case history information, and acquire patient's medical activity information, draw from case history information and medical activity information and diagnose the event, and according to diagnosing the event and generating patient's portrait of patient that draws, thereby can form more comprehensive, more three-dimensional, more complete cognition to the patient.
As shown in FIG. 4, the apparatus 400 for generating a patient representation may comprise: an acquisition module 410, an extraction module 420, and a generation module 430.
The obtaining module 410 is configured to obtain medical record information of a patient and obtain medical activity information of the patient. Note that the medical record information described in this embodiment may be information described in an electronic medical record of a patient.
In the embodiment of the present application, the information recorded in the electronic medical record may include one or more of disease information, symptom information, drug information, test information, examination information, current medical history, and the like. The medical activity information can include key medical events, key medical nodes, such as admission events, operation events, consultation events, major treatment change events, patient status relegation events, and the like of the whole process of patient treatment, namely, a series of medical activities which are developed around the process of patient treatment and recorded on a medical record during the period from patient admission to discharge. 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, examination, operation and the like, the clinical procedures can include admission, discharge, transfer and the like, and the patient conditions can include critical events, serious events, death events and the like.
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 relevant person (e.g., a medical care provider), wherein the storage space is not limited to a physical-based storage space such as 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 physician can store relevant interrogation information in a computer to form an electronic medical record (i.e., medical record information) during an interrogation of a patient; the hospital personnel can store the medical activity information in a computer during the treatment of the patient.
Specifically, the acquisition module 410 can call medical record information and medical activity information of the patient via an electronic device (e.g., a computer).
The extraction module 420 is configured to extract the medical events from the medical record information and the medical activity information.
In the embodiment of the present application, the diagnosis and treatment event may be used to describe the medical behavior received by the patient during the whole diagnosis process, wherein the diagnosis and treatment event may include event types, such as a surgical event, a consultation event, a ward round event, and the like; event arguments, for example, for surgical events, are: surgical items, surgical time, surgical code, surgeon, surgical grade, surgical assistant, anesthesiologist, anesthesia modality, pre-operative diagnosis.
It should be noted that a hospital can form a diagnosis and treatment event knowledge system based on the diagnosis and treatment events described in this embodiment, wherein the diagnosis and treatment event knowledge system can be jointly constructed by three parties, i.e., research and development, medical experts and products, in combination with the latest concept definition of the industry on the diagnosis and treatment events; moreover, according to the diagnosis and treatment event extraction requirement of the quality control of the medical records, the event type judgment and the related event argument extraction are carried out from one or more documents, the diagnosis and treatment activity progress of the whole process of the patient in the treatment is displayed by the event granularity, and the medical behavior can be truly reflected.
In an embodiment of the present application, the extraction module 420 may extract the medical events from the medical record information and the medical activity information based on natural language processing techniques and a medical knowledge graph.
The generating module 430 is configured to generate a patient representation of the patient based on the extracted clinical event.
It should be noted that the patient image described in this embodiment may be data collected such as individual population information of the patient, and a large number of electronic medical records, physical examination reports, etc. generated during clinical treatment, periodic physical examination, chronic disease monitoring, etc. As will be readily appreciated, a patient image is "tagged" to a patient, and a tag is typically a highly identifiable identifier that identifies the patient in a number of dimensions, such as age, gender, region, preferences, family history, past medical history, and the like.
Specifically, after the medical record information and the medical activity information of the patient are called out by the obtaining module 410, the extracting module 420 may extract the medical events from the medical record information and the medical activity information by using natural language processing technology and a medical knowledge graph, and then the generating module 430 generates the patient representation of the patient according to the extracted medical events.
It should be noted that the medical knowledge map described in this embodiment may be stored in a storage space of the electronic device in advance, so as to be called for use when necessary.
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, more three-dimensional and more complete cognition on the patient can be formed, and the application of a clinical AI technology is facilitated.
In an embodiment of the present application, the extracting module 420 is specifically configured to extract a plurality of entities from the medical record information and the medical activity information, generate a plurality of medical events according to the medical knowledge graph and the plurality of entities, and extract event arguments and event types of each medical event.
In one embodiment of the present application, as shown in fig. 4, the apparatus 400 for generating a patient representation may further include an input module 440, wherein the input module 440 is configured to input medical record information and medical activity information to a medical event inference model, wherein the medical event inference model generates event relationships between a plurality of medical events according to a medical knowledge graph.
In an embodiment of the present application, the generating module 430 is specifically configured to extract event occurrence times from event arguments of a plurality of clinical events, sort the plurality of clinical events according to time of the plurality of clinical events according to a time sequence, and display an event relationship among the plurality of clinical events in real time, and event arguments and event types corresponding to the clinical events respectively in the patient image.
In one embodiment of the present application, as shown in fig. 4, the apparatus 400 for generating a patient representation may further include a processing module 450, wherein the processing module 450 is configured to perform a normalization process on an entity in a plurality of clinical events.
It should be noted that the explanation of the embodiment of the patient image generation method is also applicable to the patient image generation apparatus of this embodiment, and will not be described herein again.
The patient portrait generation device of the embodiment of the application acquires medical record information of a patient and medical activity information of the patient through the acquisition module, extracts diagnosis and treatment events from the medical record information and the medical activity information through the extraction module, and generates a patient portrait of the patient according to the extracted diagnosis and treatment events through the generation module. Therefore, more comprehensive, more three-dimensional and more complete cognition on the patient can be formed, and the application of the clinical AI technology is facilitated.
There is also provided, in accordance with an embodiment of the present application, an electronic device, a readable storage medium, and a computer program product.
FIG. 5 illustrates a schematic block diagram of an example electronic device 500 that can 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 phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 5, the apparatus 500 includes a computing unit 501, which can perform various appropriate 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 calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 505 such as a magnetic disk, 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 through a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 501 performs the various methods and processes described above, such as a patient representation generation method. For example, in some embodiments, the method of generating a patient representation may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as 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 loaded into RAM 503 and executed by the computing unit 501, may perform one or more of the steps of the patient representation generation method described above. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the patient representation generation method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a 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 that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present application may be written in any combination of one or more programming languages. These program codes 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 codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. 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. A 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 a pointing device (e.g., a mouse or a 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 can 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, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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, and blockchain networks.
The computer system may include clients and servers. A client and server are generally 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 as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (13)
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 clinical event.
2. A method of generating a patient representation as defined in claim 1, wherein said extracting a clinical event from said medical record information and said medical activity information comprises:
extracting a plurality of entities from among 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 plurality of entities, and extracting event arguments and event types of each diagnosis and treatment event.
3. A method of generating a patient representation as claimed in claim 2, 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.
4. A method of generating a patient representation of a patient in accordance with the extracted clinical event as claimed in claim 2, comprising:
extracting event occurrence time from the event arguments of the plurality of diagnosis and treatment events;
sequencing the diagnosis and treatment events according to the time of the diagnosis and treatment events according to the time sequence, and displaying the event relation among the real-time diagnosis and treatment events, and the event argument and the event type which are respectively corresponding to the diagnosis and treatment events in the patient image.
5. A method of generating a patient representation as claimed in claim 4, further comprising:
and carrying out normalization processing on entities in the plurality of diagnosis and treatment events.
6. A patient representation generation 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 generating module for generating a patient representation of the patient according to the extracted diagnosis and treatment event.
7. The patient representation generation apparatus of claim 6, wherein the extraction module is specifically configured to:
extracting a plurality of entities from among 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 plurality of entities, and extracting event arguments and event types of each diagnosis and treatment event.
8. A patient representation generation apparatus as claimed in claim 7 further comprising:
and 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.
9. The patient representation generation apparatus of claim 7, wherein the generation module is specifically configured to:
extracting event occurrence time from the event arguments of the plurality of diagnosis and treatment events;
sequencing the diagnosis and treatment events according to the time of the diagnosis and treatment events according to the time sequence, and displaying the event relation among the real-time diagnosis and treatment events, and the event argument and the event type which are respectively corresponding to the diagnosis and treatment events in the patient image.
10. A patient representation generation apparatus as claimed in claim 9 further comprising:
and the processing module is used for carrying out normalization processing on entities in the plurality of diagnosis and treatment events.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of generating a patient representation according to any one of claims 1-5.
12. A non-transitory computer readable storage medium having computer instructions stored thereon for causing the computer to perform the method of generating a patient representation according to any one of claims 1-4.
13. A computer program product comprising a computer program which, when executed by a processor, implements a method of generating a patient representation according to any of claims 1 to 5.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112992362A (en) * | 2021-04-19 | 2021-06-18 | 北京妙医佳健康科技集团有限公司 | Body state evaluation device, electronic equipment and storage medium |
| CN113590775A (en) * | 2021-06-22 | 2021-11-02 | 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 |
| CN117116407A (en) * | 2023-10-23 | 2023-11-24 | 之江实验室 | Patient portrait generating method, device and storage medium based on electronic medical record |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050273362A1 (en) * | 2004-06-02 | 2005-12-08 | Catalis, Inc. | Method and system for generating medical narrative |
| US20140297323A1 (en) * | 2013-03-27 | 2014-10-02 | International Business Machines Corporation | Extracting key action patterns from patient event data |
| 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 |
-
2020
- 2020-12-11 CN CN202011460427.1A patent/CN112562808B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050273362A1 (en) * | 2004-06-02 | 2005-12-08 | Catalis, Inc. | Method and system for generating medical narrative |
| US20140297323A1 (en) * | 2013-03-27 | 2014-10-02 | International Business Machines Corporation | Extracting key action patterns from patient event data |
| 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 |
|---|
| 姚华彦;张鑫金;何萍;: "基于大数据的患者画像标签体系构建方法及应用研究", 中国卫生信息管理杂志, no. 06 * |
| 李方方;陈容华;秦胜男;戴胜;: "基于互联网大数据的事件智能抓取和画像", 无线互联科技, no. 01 * |
| 田娟;朱定局;杨文翰;: "基于大数据平台的企业画像研究综述", 计算机科学, no. 2 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112992362A (en) * | 2021-04-19 | 2021-06-18 | 北京妙医佳健康科技集团有限公司 | Body state evaluation device, electronic equipment and storage medium |
| 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 |
| CN113590775A (en) * | 2021-06-22 | 2021-11-02 | 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 |
| 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 |
| CN117116407A (en) * | 2023-10-23 | 2023-11-24 | 之江实验室 | Patient portrait generating method, device and storage medium based on electronic medical record |
| 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 |
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|---|---|
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