WO2022160614A1

WO2022160614A1 - Method and apparatus for constructing medical entity relationship diagram, method and apparatus for medical order quality control, device, and medium

Info

Publication number: WO2022160614A1
Application number: PCT/CN2021/106769
Authority: WO
Inventors: 施振辉; 夏源; 陆超; 代小亚; 黄海峰
Original assignee: 北京百度网讯科技有限公司
Priority date: 2021-01-29
Filing date: 2021-07-16
Publication date: 2022-08-04
Also published as: CN112786144B; CN112786144A

Abstract

Provided are a method and apparatus for constructing a medical entity relationship diagram, a method and apparatus for medical order quality control, a device, and a medium. The method for constructing a medical entity relationship diagram comprises: performing entity recognition on medical related data to obtain at least two medical entities (S101); determining drug classification labels associated with the at least two medical entities (S102); and constructing a medical entity relationship diagram according to the at least two medical entities and the drug classification labels associated with the at least two medical entities (S103).

Description

Method and device for constructing medical entity relationship diagram, method and device, device and medium for quality control of doctor's orders

This application claims the priority of a Chinese patent application with application number 202110129851.6 filed with the China Patent Office on January 29, 2021, the entire contents of which are incorporated herein by reference.

technical field

The present application relates to the field of artificial intelligence technology, such as deep learning technology, such as a method and device for constructing a medical entity relationship graph, and a method and device, equipment, and medium for quality control of medical orders.

Background technique

In clinical practice, doctors make different medical orders according to different conditions of patients. However, sometimes the unreasonable medication in the medical orders will lead to medical damage, which will lead to medical disputes. Therefore, how to accurately test the rationality of drug use in the medical orders made by doctors has become an urgent problem to be solved.

SUMMARY OF THE INVENTION

The present application provides a method and device for constructing a medical entity relationship diagram, a method and device, equipment and medium for quality control of a doctor's order.

Provides a method for building a medical entity relationship diagram, including:

Perform entity recognition on medical-related data to obtain at least two medical entities;

Identifying drug classification labels associated with at least two medical entities;

A medical entity relationship graph is constructed according to the at least two medical entities and the drug classification labels associated with the at least two medical entities.

A method for quality control of doctor's orders is also provided, including:

Obtain the first medical entity and the second medical entity from the medical order medical record data to be detected; wherein, the first medical entity refers to the medical entity whose entity type is symptom, and the second medical entity refers to the medical entity whose entity type is medicine;

Using an entity relationship diagram, the quality control information between the first medical entity and the second medical entity is determined; wherein, the entity relationship diagram is constructed by using the above method for constructing a medical entity relationship diagram.

Also provided is an apparatus for constructing a medical entity relationship diagram, including:

The entity recognition module is set to perform entity recognition on medical-related data to obtain at least two medical entities;

The label prediction module is configured to determine the drug classification labels associated with at least two medical entities;

The relationship diagram building module is configured to construct a medical entity relationship diagram according to the at least two medical entities and the drug classification labels associated with the at least two medical entities.

Also provided is a device for quality control of a doctor's order, comprising:

The entity acquisition module is configured to acquire the first medical entity and the second medical entity from the medical order medical record data to be detected; wherein, the first medical entity refers to the medical entity whose entity type is symptom, and the second medical entity refers to the entity type is the medical entity of the drug;

The quality control module is configured to use an entity relationship diagram to determine the quality control information between the first medical entity and the second medical entity; wherein the entity relationship diagram is constructed by using the above method for constructing a medical entity relationship diagram.

Also provided is 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, and the instructions are executed by the at least one processor, so that the at least one processor can execute the above-mentioned method for constructing a medical entity relationship diagram or a method for quality control of a doctor's order.

A non-transitory computer-readable storage medium storing computer instructions is also provided, and the computer instructions are used to cause a computer to execute the above-mentioned method for constructing a medical entity relationship diagram or method for quality control of a doctor's order.

A computer program product is also provided, including a computer program. When the computer program is executed by a processor, the above-mentioned method for constructing a medical entity relationship diagram or a method for quality control of a doctor's order is implemented.

Description of drawings

1 is a schematic flowchart of a method for constructing a medical entity relationship diagram provided by an embodiment of the present application;

2 is a schematic flowchart of another method for constructing a medical entity relationship diagram provided by an embodiment of the present application;

3 is a schematic flowchart of a method for quality control of a doctor's order provided by an embodiment of the present application;

4 is a schematic flowchart of another method for quality control of a doctor's order provided by an embodiment of the present application;

5 is a schematic structural diagram of an apparatus for constructing a medical entity relationship diagram provided by an embodiment of the present application;

6 is a schematic structural diagram of a device for quality control of medical orders provided by an embodiment of the present application;

FIG. 7 is a block diagram of an electronic device for implementing a method for constructing a medical entity relationship diagram and a method for quality control of a doctor's order provided by an embodiment of the present application.

Detailed ways

Exemplary embodiments of the present application are described below with reference to the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and should be considered as exemplary only. Descriptions of well-known functions and constructions are omitted in the following description.

In the embodiment of the present application, the quality control of doctor's orders is different from clinical auxiliary diagnosis, intelligent consultation or drug recommendation. It does not give recommendations or suggestions based on the information of medical records. The abnormality of the doctor's prescription (man-made abnormality or abnormality caused by the doctor's omission), that is, the quality control of the doctor's order is more inclined to abnormal detection. To perform anomaly detection, it is necessary to first learn the relationship between the patient's condition and the drug, as well as the relationship between the drug and the drug. Therefore, the embodiments of the present application learn the relationship between medical entities by constructing a knowledge graph.

1 is a schematic flowchart of a method for constructing a medical entity relationship diagram provided by an embodiment of the present application. This embodiment can be applied to a situation where a medical entity relationship diagram is constructed by adding a label node, and then the medical entity relationship diagram is used for quality control of medical orders. . The method can be performed by an apparatus for constructing a medical entity relationship diagram, which is implemented in software and/or hardware and integrated on an electronic device.

Referring to Figure 1, the method for constructing a medical entity relationship diagram is as follows:

S101. Perform entity recognition on medical-related data to obtain at least two medical entities.

Medical-related data can optionally be text data, including at least one of the following: drug instructions, medical books, medical guides, and users' medical orders and medical records; entities refer to things in the real world, such as people, place names, companies, telephones, Animals, etc., so medical entities refer to things in the medical field, such as drug names, phrases that describe symptoms of diseases. In an optional implementation manner, a natural language processing technology may be used to perform entity recognition on medical-related data to obtain at least two medical entities.

S102. Determine the drug classification label associated with the medical entity.

Due to the rationality of drug use in the data of medical orders and medical records, learning the relationship between diseases and drugs based on erroneous data will lead to large errors in subsequent predictions. The preference for the condition should use a class of drugs, rather than a direct description of the drug name, ie the lack of a relationship between the condition and the drug. Based on this, in the embodiment of this application, a drug classification label is introduced, and each drug classification label is pre-associated with at least one drug name, so it is only necessary to determine the drug classification label associated with a medical entity (such as an entity describing disease symptoms), That is, the relationship between the drug name and the symptoms can be established through the drug classification label, that is, the relationship between at least two medical entities is established.

S103 , constructing a medical entity relationship diagram according to the medical entity and the drug classification label.

In an optional embodiment, constructing a medical entity relationship graph according to the medical entity and the drug classification label includes: using the medical entity and the drug classification label as nodes, and using the relationship between the at least two medical entities (for example, the symptom and the The relationship between drugs), the relationship between medical entities and drug classification labels (such as the relationship between symptoms and labels or the relationship between drugs and labels), and the relationship between at least two drug classification labels are used as edges to construct a relationship graph of medical entities. Then use the medical entity relationship diagram to carry out the quality control of the doctor's order.

In the embodiment of the present application, a medical entity relationship diagram is established by introducing a drug classification label, and the relationship between at least two medical entities is accurately determined; further, the medical order quality control is performed based on the medical entity relationship diagram, which provides a guarantee for the medical order quality control. Improve the accuracy of the quality control of doctor's orders.

FIG. 2 is a schematic flowchart of another method for constructing a medical entity relationship diagram provided by an embodiment of the present application. This embodiment is described on the basis of the foregoing embodiment. Referring to Figure 2, the method for constructing a medical entity relationship diagram is as follows:

S201. Based on the medical natural language processing model, perform word segmentation processing on medical-related data, and perform entity recognition on the word segmentation result to obtain at least two medical entities.

The medical-related data includes at least one of the following: drug instructions, medical books, medical guides, and the user's medical order data; the medical natural language processing model is based on a bi-directional long-short-term memory network (Bi-directional Long-Short Term Memory, Bi-LSTM) , Attention mechanism (Attention) and a deep network model constructed by Condition Random Field (CRF). The deep network model based on Bi-LSTM+CRF, compared with the traditional neural network (Deep Neural Networks (DNN), Recurrent Neural Network (RNN)) framework, on the one hand, considers the words in the text data. The order relationship with words is more in line with the basic assumption of natural language processing (word order affects the expression of semantics). The problems of gradient explosion and gradient vanishing make model training more stable. At the same time, the introduction of an attention mechanism can precisely determine the medical entities that need to be focused on.

Exemplarily, the patient's main complaint description in the medical record data: "I have a cough, a headache, a stomachache and a cold today." The medical natural language processing module first divides this sentence into words to obtain "I, today, cough, headache, stomachache, and a little cold", Then perform entity recognition, and the obtained medical entities include "cough, headache, stomach ache, cold". Through entity type identification, it can be known that "cough, headache, stomach ache" is a symptom-based medical entity, and "cold" belongs to a disease-based medical entity.

S202. Use a pre-trained label prediction model to perform label prediction on the medical entity, and determine the drug classification label associated with the medical entity according to the prediction result.

The prediction result is the output result of the label prediction model, including the predicted drug classification label and probability. Optionally, the medical entity is used as the input of the label prediction model, and according to the output of the label prediction model, the probability value that the medical entity is associated with a drug classification label is obtained, and then the relationship between the medical entity and the drug classification label is determined according to the probability value. For example, if the probability value is greater than the preset threshold, it is considered that there is a relationship between the two; if the probability value is less than the preset threshold, it is considered that there is no relationship between the two.

In the embodiment of this application, the label prediction model is a knowledge-enhanced semantic representation model (Enhanced Representation from kNowledge IntEgration, ERNIE); the sample data for training the label prediction model is the entity description data determined from the drug instructions and the drugs corresponding to the entity description data Category labels. Exemplarily, construct the sample data in the form of a 2-tuple, for example, construct a 2-tuple of <entities, label>, entities is the entity description data related to the drug in a drug instruction manual, such as "used to relieve mild to moderate pain such as Arthralgia, muscle pain, neuralgia, headache, migraine, toothache, dysmenorrhea, also used for the entity in "fever caused by common cold or influenza", label is the drug classification label corresponding to the entity description data, such as antibiotics, etc. .

The label prediction model is trained based on the entity and drug classification labels in the item description, so using the label prediction model for prediction can improve the efficiency and accuracy of determining the drug classification labels associated with medical entities.

S203 , constructing a medical entity relationship diagram according to the medical entity and the drug classification label.

In the embodiment of the present application, entity recognition is performed based on a medical natural language processing model to improve the recognition accuracy; and the drug classification label associated with the entity is predicted by the label prediction model, which can improve the efficiency and accuracy of determining the drug classification label associated with the medical entity .

FIG. 3 is a schematic flowchart of a method for quality control of a doctor's order provided by an embodiment of the present application. This embodiment is described on the basis of the foregoing embodiment. Referring to Figure 3, the method for quality control of doctor's orders is as follows:

S301. Acquire a first medical entity and a second medical entity from the medical order medical record data to be detected.

The first medical entity refers to a medical entity whose entity type is symptom, and the second medical entity refers to a medical entity whose entity type is medicine.

In an optional implementation manner, obtaining the first medical entity and the second medical entity from the medical order medical record data to be detected, including:

Based on the medical natural language processing model, word segmentation is performed on the medical order and medical record data to be detected, and entity recognition is performed on the word segmentation results to obtain at least two medical entities. For the identification process, please refer to the description of the above embodiment; Identifying; wherein, the entity type includes at least one of the following: symptoms, diseases, examinations, tests, surgery, and medicines; and obtaining the first medical entity and the second medical entity according to the entity type identification result.

By performing instance type identification on at least two medical entities, the efficiency of acquiring the first medical entity and the second medical entity can be guaranteed.

S302. Using an entity relationship diagram, determine the quality control information between the first medical entity and the second medical entity.

For the construction of the entity relationship diagram, reference may be made to the foregoing embodiments, and details are not described herein again. According to the entity relationship diagram, if it is determined that there is an association relationship between the first medical entity and the second medical entity, for example, there is a connection path from the first medical entity to the second medical entity in the entity relationship diagram, then it is determined that there is an association relationship between the two , there is no need for quality control reminder; if there is no connection path from the first medical entity to the second medical entity in the entity relationship diagram, it is determined that the two are not related, and it is considered that the doctor's prescription issued by the doctor is in the patient's electronic medical record. In the information, there is no disease support. It may be that the doctor prescribes the wrong medicine due to negligence. On the other hand, it may be that the doctor deliberately prescribes the medicine to cheat the insurance, thus triggering the quality control reminder.

In the embodiment of the present application, since at least two entities in the entity relationship diagram are connected through drug classification labels, it is only necessary to determine whether there is a connection path between the two entities to determine whether the two entities are related, and then determine whether to trigger the quality control Reminder, which can improve the efficiency and accuracy of the quality control of doctor's orders.

FIG. 4 is a schematic flowchart of another method for quality control of doctor’s orders provided in the embodiment of the present application. This embodiment is described on the basis of the above-mentioned embodiment. Referring to FIG. 4 , the method for quality control of doctor’s orders is as follows:

S401. Acquire a first medical entity and a second medical entity from the medical order medical record data to be detected.

S402. Using a preset traversal algorithm, determine from the entity relationship graph at least one connection path with the first medical entity as the starting node and the second medical entity as the ending node.

The preset traversal algorithm can optionally be a breadth first search algorithm (BFS), or can be other traversal algorithms, such as a depth traversal algorithm, which is not limited here. In the embodiment of the present application, if it is determined by the breadth-first traversal algorithm that there are multiple connection paths with the first medical entity as the starting node and the second medical entity as the ending node, in order to improve the accuracy of the quality control of the doctor's order, it is necessary to The connected paths are determined according to the steps of S403-S404.

S403. Determine a target connected path with the shortest path from at least one connected path, and calculate an association score of the target connected path.

The shorter the connection path, the greater the possibility that the first medical entity and the second medical entity are associated, wherein the length of the connection path can be determined by the number of drug classification label nodes passed by the first medical entity to the second medical entity Sure. Therefore, a target connected path with the shortest path can be determined from at least one connected path, and an association score of the target connected path can be calculated.

In an optional embodiment, if the target connection path is composed of a first medical entity, a second medical entity and at least one drug classification label, the process of calculating the correlation score of the target connection path includes: determining the first medical entity The entity is associated with the first probability value of the drug classification label, and the second medical entity is associated with the second probability value of the drug classification label; the first probability value and the second probability value are weighted to obtain an association score. By weighting the probability values of the tags associated with the first medical entity and the second medical entity, the efficiency of calculating the association score can be improved.

Exemplarily, it is assumed that the connected path consists of three nodes: V _drug (drug node), V _tag (tag node) and V _disease (disease node), wherein P _drug-tag represents the edge between nodes V _drug and V _tag Weight, edge weight P _drug-tag can optionally be the probability of the node V _drug associated with the node V _tag , similarly, P _disease-tag can be the probability of the disease node associated with the node V _tag , α (α>=1) represents the path length (when α>1, the longer the path, the faster the final score decays), the final result P _reminder is calculated based on the path weight. Here is an example of a path with three nodes, which constitutes two edges. In practical applications, edges composed of more nodes can be considered. The calculation formula is as follows: P _reminder =(P _drug-tag +P _disease-tag )/N ^α , where N represents the number of nodes.

S404. Determine the quality control information between the first medical entity and the second medical entity according to the correlation score.

In the embodiment of the present application, in order to ensure the accuracy of quality control, an alarm threshold may be preset, and when the correlation score is lower than the alarm threshold, a quality control reminder is triggered.

In the embodiment of the present application, by calculating the association score of the connection path and evaluating the association relationship between the first medical entity and the second medical entity constituting the connection path, the accuracy of the quality control can be ensured.

FIG. 5 is a schematic structural diagram of an apparatus for constructing a medical entity relationship diagram provided by an embodiment of the present application. As shown in FIG. 5 , the apparatus includes: an entity identification module 501, configured to perform entity identification on medical-related data, and obtain at least two The label prediction module 502 is configured to determine the drug classification label associated with the medical entity; the relationship diagram construction module 503 is configured to construct a medical entity relationship diagram according to the medical entity and the drug classification label.

On the basis of the above embodiment, optionally, the relationship diagram building module is set as:

Use medical entities and drug classification labels as nodes, and use the relationship between at least two medical entities, the relationship between medical entities and drug classification labels, and the relationship between at least two drug classification labels as edges to construct the relationship between medical entities picture.

On the basis of the above embodiment, optionally, the entity identification module is set to:

Based on the medical natural language processing model, word segmentation is performed on medical-related data, and entity recognition is performed on the word segmentation results to obtain at least two medical entities; wherein, the medical-related data includes at least one of the following: drug instructions, medical books, medical guidelines and The user's doctor's order and medical record data; the medical natural language processing model is a deep network model constructed based on a bidirectional long-term and short-term memory network, an attention mechanism and a conditional random field.

On the basis of the above embodiment, optionally, the label prediction module is set to:

The pre-trained label prediction model is used to predict the labels of medical entities, and the drug classification labels associated with the medical entities are determined according to the prediction results.

On the basis of the above embodiment, optionally, the label prediction model is a knowledge-enhanced semantic representation model; the sample data for training the label prediction model is entity description data and entity description data determined from drug instructions, medical books and medical guides The corresponding drug classification label.

The apparatus for constructing a medical entity relationship diagram provided by the embodiment of the present application can execute the method for constructing a medical entity relationship diagram provided by any embodiment of the present application, and has corresponding functional modules and effects of the execution method. For the content not described in detail in this embodiment, reference may be made to the description in any method embodiment of this application.

FIG. 6 is a schematic structural diagram of a device for quality control of medical orders provided by an embodiment of the present application. As shown in FIG. 6 , the device includes: an entity acquisition module 601 configured to acquire a first medical entity from medical order medical record data to be detected and a second medical entity; wherein, the first medical entity refers to a medical entity whose entity type is a symptom, and the second medical entity refers to a medical entity whose entity type is a drug; the quality control module 602 is configured to use an entity relationship diagram to determine the first medical entity. Quality control information between a medical entity and a second medical entity; wherein, the entity relationship diagram is constructed by using the above method for constructing a medical entity relationship diagram.

On the basis of the above embodiment, optionally, the quality control module includes:

The traversal unit is set to adopt a preset traversal algorithm to determine from the entity relationship graph at least one connected path with the first medical entity as the starting node and the second medical entity as the terminating node; the screening calculation unit is set to start from at least one path The shortest target connection path among the connection paths is determined, and the correlation score of the target connection path is calculated; the quality control unit is set to determine the quality control information between the first medical entity and the second medical entity according to the correlation score.

On the basis of the above embodiment, optionally, if the target connection path is composed of a first medical entity, a second medical entity and at least one drug classification label, the screening calculation unit includes:

The probability value determination subunit is set to determine the first probability value of the associated drug classification label of the first medical entity, and the second probability value of the associated drug classification label of the second medical entity; the calculation subunit is set to determine the first probability value and the second probability value of the associated drug classification label; The second probability value is weighted to obtain an association score.

On the basis of the foregoing embodiment, optionally, the entity acquisition module includes:

The entity recognition unit is set to perform word segmentation processing on the medical order medical record data to be detected based on the medical natural language processing model, and performs entity recognition on the word segmentation results to obtain at least two medical entities; the type recognition unit is set to the entity type of the medical entity. performing identification; wherein, the entity type includes at least one of the following: symptoms, diseases, examinations, tests, operations and medicines; the acquiring unit is configured to acquire the first medical entity and the second medical entity according to the entity type identification result.

The device for quality control of a doctor's order provided by the embodiment of the present application can execute the method for quality control of a doctor's order provided by any embodiment of the present application, and has functional modules and effects corresponding to the execution method. For the content not described in detail in this embodiment, reference may be made to the description in any method embodiment of this application.

In the technical solution of this application, the acquisition, storage and application of the user's personal information involved are in compliance with the relevant laws and regulations, and do not violate public order and good customs.

According to the embodiments of the present application, the present application further provides an electronic device, a readable storage medium, and a computer program product.

FIG. 7 is a block diagram of an electronic device 700 for implementing a method for constructing a medical entity relationship diagram and a method for quality control of a doctor's order provided by an embodiment of the present application. Electronic device 700 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 700 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are by way of example only, and are not intended to limit implementations of the application described and/or claimed herein.

As shown in FIG. 7 , the electronic device 700 includes a computing unit 701, which can be loaded into a random access memory (Random Access Memory) according to a computer program stored in a read-only memory (Read-Only Memory, ROM) 702 or from a storage unit 708, A computer program in RAM) 703 to perform various appropriate actions and processes. In the RAM 703, various programs and data required for the operation of the electronic device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An Input/Output (I/O) interface 705 is also connected to the bus 704 .

Various components in the electronic device 700 are connected to the I/O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, an optical disk etc.; and a communication unit 709, such as a network card, modem, wireless communication transceiver, and the like. The communication unit 709 allows the electronic device 700 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.

Computing unit 701 may be a variety of general-purpose and/or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU), various dedicated artificial intelligence (Artificial Intelligence, AI) computing chips, various operating A computational unit, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc., for the algorithm of the machine learning model. The computing unit 701 executes the methods and processes described above, such as a method for quality control of a doctor's order or a method for constructing a medical entity relationship diagram. For example, in some embodiments, a method of quality control or constructing a medical entity relationship diagram may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 708 . In some embodiments, part or all of the computer program may be loaded and/or installed on electronic device 700 via ROM 702 and/or communication unit 709 . When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the above-described method for quality control of a doctor's order or a method for constructing a medical entity relationship diagram can be performed. Alternatively, in other embodiments, the computing unit 701 may be configured in any other suitable manner (eg, by means of firmware) to perform a method of quality control of a doctor's order or a method of constructing a medical entity relationship diagram.

Various implementations of the systems and techniques described herein above may be implemented in digital electronic circuitry, integrated circuit systems, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Parts (ASSP), System on Chip (SoC), Complex Programming Logic Device (CPLD), computer hardware, firmware, software, and/or them implemented in a combination. These various embodiments may include implementation in one or more computer programs executable and/or interpretable on a programmable system including at least one programmable processor that The processor, which may be a special purpose or general-purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device an 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 code, when executed by the processor or controller, performs the functions/functions specified in the flowcharts and/or block diagrams. Action is implemented. The program code may execute entirely on the machine, partly on the machine, partly on the machine and partly on a remote machine as a stand-alone software package or entirely on the remote machine or server.

In the context of this application, a machine-readable medium may be a tangible medium that may contain or store the program for use by or in connection with the instruction execution system, apparatus or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any suitable combination of the foregoing. Examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (Erasable Programmable Read-Only Memory, EPROM or flash memory), fiber optics, Compact Disc Read-Only Memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing .

To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having a display device (eg, a Cathode Ray Tube (CRT) or a liquid crystal display) configured to display information to the user (Liquid Crystal Display, LCD monitor); and a keyboard and pointing device (eg, mouse or trackball) through which a user can provide input to the computer. Other kinds of devices may also be configured to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (eg, visual feedback, auditory feedback, or tactile feedback); and may be in any form (including acoustic input, voice input, or tactile input) to receive input from the user.

The systems and techniques described herein may be implemented on a computing system that includes back-end components (eg, as a data server), or a computing system that includes middleware components (eg, an application server), or a computing system that includes front-end components (eg, a user's computer having a graphical user interface or web browser through which a user may interact with implementations of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks include: Local Area Network (LAN), Wide Area Network (WAN), blockchain network, and the Internet.

A computer system can include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or a cloud host. It is a host product in the cloud computing service system to solve the problems existing in traditional physical host and virtual private server (Virtual Private Server, VPS) services. The management is difficult and the business expansion is weak. The server can also be a server of a distributed system, or a server combined with a blockchain.

Steps can be reordered, added, or removed using the various forms of flow shown above. For example, multiple steps described in the present application may be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, no limitation is imposed herein.

Claims

A method for building a medical entity relationship diagram, comprising:

Perform entity recognition on medical-related data to obtain at least two medical entities;

determining a drug classification label associated with the at least two medical entities;

A medical entity relationship graph is constructed according to the at least two medical entities and the drug classification labels associated with the at least two medical entities.
The method according to claim 1, wherein the constructing a medical entity relationship diagram according to the at least two medical entities and the drug classification label comprises:

Use each medical entity and the drug classification label associated with each medical entity as a node, and classify the relationship between the at least two medical entities and the drug classification associated with each medical entity and each medical entity The relationship between the tags and the relationship between the drug classification tags associated with the at least two medical entities are used as edges to construct a relationship graph of the medical entities.
The method according to claim 1, wherein, performing entity identification on medical-related data to obtain at least two medical entities, comprising:

Based on a medical natural language processing model, word segmentation is performed on the medical-related data, and entity recognition is performed on the word segmentation result to obtain the at least two medical entities;

Wherein, the medical-related data includes at least one of the following: drug instructions, medical books, medical guides, and user's medical order data; the medical natural language processing model is based on a bidirectional long short-term memory network, an attention mechanism, and a conditional random field The built deep network model.
The method of claim 1, wherein the determining of the drug classification labels associated with the at least two medical entities comprises:

Use a pre-trained label prediction model to perform label prediction on the at least two medical entities, and determine the drug classification labels associated with the at least two medical entities according to the prediction results.
The method according to claim 4, wherein the label prediction model is a knowledge-enhanced semantic representation model; the sample data for training the label prediction model is the entity description data determined from the drug instructions corresponding to the entity description data drug classification labels.
A method for quality control of doctor's orders, including:

Obtain a first medical entity and a second medical entity from the medical order medical record data to be detected; wherein, the first medical entity refers to a medical entity whose entity type is symptom, and the second medical entity refers to a medical entity whose entity type is medicine ;

Using an entity relationship diagram, the quality control information between the first medical entity and the second medical entity is determined; wherein, the entity relationship diagram adopts the construction medical entity according to any one of claims 1-5. The method of building a relationship diagram.
The method according to claim 6, wherein the using an entity relationship diagram to determine the quality control information between the first medical entity and the second medical entity comprises:

Using a preset traversal algorithm, determine from the entity relationship graph at least one connected path with the first medical entity as a starting node and the second medical entity as a termination node;

Determine a target connected path with the shortest path from the at least one connected path, and calculate an association score of the target connected path;

According to the correlation score, quality control information between the first medical entity and the second medical entity is determined.
7. The method of claim 7, wherein the calculating the target connectivity is performed where the target connectivity path is composed of the first medical entity, the second medical entity, and at least one drug classification label The associated score for the path, including:

determining a first probability value associated with the at least one drug classification label by the first medical entity, and a second probability value associated with the at least one drug classification label by the second medical entity;

The first probability value and the second probability value are weighted to obtain the association score.
The method according to claim 6, wherein the obtaining the first medical entity and the second medical entity from the medical order medical record data to be detected comprises:

Based on a medical natural language processing model, word segmentation is performed on the medical order medical record data to be detected, and entity recognition is performed on the word segmentation result to obtain at least two medical entities;

Identifying entity types of the at least two medical entities; wherein the entity types include at least one of the following: symptoms, diseases, examinations, tests, surgery, and medicines;

Obtain the first medical entity and the second medical entity according to the entity type identification result.
A device for constructing a medical entity relationship diagram, comprising:

The entity recognition module is set to perform entity recognition on medical-related data to obtain at least two medical entities;

The label prediction module is configured to determine the drug classification labels associated with the at least two medical entities;

The relationship graph building module is configured to build a medical entity relationship graph according to the at least two medical entities and the drug classification labels associated with the at least two medical entities.
The apparatus of claim 10, wherein the relationship graph building module is configured to:

Use each medical entity and the drug classification label associated with each medical entity as a node, and classify the relationship between the at least two medical entities and the drug classification associated with each medical entity and each medical entity The relationship between the tags and the relationship between the drug classification tags associated with the at least two medical entities are used as edges to construct a relationship graph of the medical entities.
The device according to claim 10, wherein the entity identification module is configured to:

Based on a medical natural language processing model, word segmentation is performed on the medical-related data, and entity recognition is performed on the word segmentation result to obtain the at least two medical entities;

Wherein, the medical-related data includes at least one of the following: drug instructions, medical books, medical guides, and user's medical order data; the medical natural language processing model is based on a bidirectional long short-term memory network, an attention mechanism, and a conditional random field The built deep network model.
The device according to claim 10, wherein the label prediction module is configured to:

Use a pre-trained label prediction model to perform label prediction on the at least two medical entities, and determine drug classification labels associated with the at least two medical entities according to the prediction results.
The device according to claim 13, wherein the label prediction model is a knowledge-enhanced semantic representation model; the sample data for training the label prediction model is the entity description data determined from the drug instructions corresponding to the entity description data drug classification labels.
A device for quality control of doctor's orders, comprising:

The entity acquisition module is configured to acquire a first medical entity and a second medical entity from the medical order medical record data to be detected; wherein, the first medical entity refers to a medical entity whose entity type is symptom, and the second medical entity refers to an entity a medical entity of type drug;

A quality control module, configured to use an entity relationship diagram to determine the quality control information between the first medical entity and the second medical entity; wherein, the entity relationship diagram adopts any one of claims 1-5 The method for constructing a medical entity relationship diagram is constructed.
The apparatus of claim 15, wherein the quality control module comprises:

A traversal unit, configured to adopt a preset traversal algorithm, and determine, from the entity relationship graph, at least one connected path with the first medical entity as a starting node and the second medical entity as a termination node;

A screening calculation unit, configured to determine a target connected path with the shortest path from the at least one connected path, and calculate an association score of the target connected path;

A quality control unit, configured to determine quality control information between the first medical entity and the second medical entity according to the correlation score.
The apparatus according to claim 16, wherein, in the case where the target communication path is composed of the first medical entity, the second medical entity and at least one drug classification label, the screening calculation unit comprises: :

a probability value determination subunit, configured to determine a first probability value associated with the at least one drug classification label by the first medical entity, and a second probability value associated with the at least one drug classification label by the second medical entity;

The calculation subunit is configured to perform weighting processing on the first probability value and the second probability value to obtain the association score.
The apparatus of claim 15, wherein the entity acquisition module comprises:

an entity recognition unit, configured to perform word segmentation processing on the medical order medical record data to be detected based on a medical natural language processing model, and perform entity recognition on the word segmentation result to obtain at least two medical entities;

a type identification unit, configured to identify the entity types of the at least two medical entities; wherein, the entity types include at least one of the following: symptoms, diseases, examinations, tests, surgery, and medicines;

The obtaining unit is configured to obtain the first medical entity and the second medical entity according to the entity type identification result.
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, the instructions being executed by the at least one processor to enable the at least one processor to execute claims 1-5 or claims 6-9 The method of any of the above.
A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-5 or 6-9.
A computer program product comprising a computer program which, when executed by a processor, implements the method of any of claims 1-5 or 6-9.