CN116486972A - Electronic medical record generation method, device, equipment and storage medium - Google Patents
Electronic medical record generation method, device, equipment and storage medium Download PDFInfo
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- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention relates to an artificial intelligence technology, and discloses a method for generating an electronic medical record, which comprises the following steps: performing text conversion on medical dialogue voice, performing unstructured conversion on diagnosis inspection data, summarizing the converted medical dialogue text and the diagnosis inspection text to obtain medical texts, and performing entity recognition on the medical texts to obtain recognition entities and entity types of each recognition entity; vector conversion is carried out on each identification entity, and an initial entity characteristic vector of each identification entity is obtained; performing attention weighting on the initial entity feature vector to obtain a target entity feature vector; matching and screening all the identification entities by utilizing the target entity feature vector to obtain a target entity; and generating an electronic medical record based on the target entity. The invention also relates to a blockchain technology, and the medical seeking text can be stored in a blockchain node. The invention also provides an electronic medical record generating device, equipment and medium. The invention can improve the accuracy of electronic medical record generation.
Description
Technical Field
The present invention relates to artificial intelligence technology, and in particular, to a method and apparatus for generating an electronic medical record, an electronic device, and a storage medium.
Background
Along with the gradual popularization and development of intelligent medical treatment, the process of seeking medical attention of people is more and more automated, such as: the doctor needs to write the medical record originally, and the electronic medical record can be automatically generated according to the medical data of the user.
However, the existing electronic medical record generation method can only extract data from a single-dimension data source to generate the electronic medical record, and cannot completely represent the medical record of the user, so that the accuracy rate of generating the electronic medical record is low.
Disclosure of Invention
The invention provides a method, a device, electronic equipment and a storage medium for generating electronic medical records, and mainly aims to improve the accuracy of electronic medical record generation.
Acquiring medical dialogue voice and diagnosis inspection data of a user;
performing text conversion on the medical treatment dialogue voice to obtain medical treatment dialogue text, and performing unstructured conversion on the medical treatment inspection data to obtain medical treatment inspection text;
summarizing the medical treatment dialogue text and the medical treatment examination text to obtain medical treatment text, and carrying out entity identification on the medical treatment text to obtain identification entities and entity types corresponding to each identification entity;
vector conversion is carried out on each identification entity based on the entity type, and an initial entity characteristic vector of each identification entity is obtained;
performing attention weighting on the initial entity feature vector based on a self-attention mechanism to obtain a target entity feature vector;
matching and screening all the identification entities by utilizing the target entity feature vector to obtain a target entity;
and filling each target entity into a preset electronic medical record template based on the entity type to obtain the electronic medical record of the user.
Optionally, the entity recognition of the medical treatment text to obtain recognition entities and entity types corresponding to each recognition entity includes:
performing word segmentation on the medical text to obtain a plurality of word segmentation words, and converting the word segmentation words into vectors to obtain word segmentation word vectors;
extracting features of the word segmentation word vectors by using a BiLSTM model, and identifying and classifying the extracted features by using a pre-constructed classification function to obtain entity probabilities corresponding to preset field types;
determining word segmentation words corresponding to word segmentation word vectors with entity probability larger than a preset entity threshold value corresponding to the preset field type as entity words of the preset field type;
calculating the sequence coefficient of the entity words corresponding to each preset field type by using a serialization labeling algorithm, and combining all the entity words corresponding to the preset field types according to the sequence coefficient to obtain an initial recognition entity corresponding to the preset field type;
and carrying out entity alignment on the initial recognition entity to obtain a recognition entity corresponding to the initial recognition entity, and determining a preset field type corresponding to the initial recognition entity as the entity type of the recognition entity corresponding to the initial recognition entity.
Optionally, the vector conversion is performed on each of the identified entities based on the entity type to obtain an initial entity feature vector of each of the identified entities, including:
converting the identified entity into a vector to obtain an entity vector of the identified entity;
converting the entity type into a vector to obtain a type vector of the entity type;
and combining the entity vector of the identified entity with the type vector of the entity type corresponding to the identified entity to obtain the initial entity characteristic vector of the identified entity.
Optionally, the converting the identified entity into a vector to obtain an entity vector of the identified entity includes:
converting each character in the recognition entity into a vector to obtain an entity character vector;
and combining all the entity character vectors according to the sequence of the corresponding characters in the recognition entity to obtain the entity vector.
Optionally, the performing matching screening on all the identified entities by using the feature vectors of the target entity to obtain the target entity includes:
performing feature compression on the target entity feature vector by using a pre-constructed full connection layer to obtain a compressed feature value;
and calculating a pre-constructed classification function by taking the compressed characteristic value as a function variable to obtain the matching probability of the target entity characteristic vector.
And screening all the identification entities by using the matching probability and a preset matching threshold value to obtain the target entity.
Optionally, the filling each target entity into a preset electronic medical record template based on the entity type to obtain an electronic medical record of the user includes:
acquiring the region type of each region in the electronic template;
consistency comparison is carried out on the entity types and the region types so as to determine the region corresponding to each entity type;
and filling all target entities corresponding to each entity type into a region corresponding to the entity type to obtain the electronic medical record.
In order to solve the above problems, the present invention further provides an electronic medical record generating device, including:
the entity identification module is used for acquiring medical conversation voice and diagnosis inspection data of a user; performing text conversion on the medical treatment dialogue voice to obtain medical treatment dialogue text, and performing unstructured conversion on the medical treatment inspection data to obtain medical treatment inspection text; summarizing the medical treatment dialogue text and the medical treatment examination text to obtain medical treatment text, and carrying out entity identification on the medical treatment text to obtain identification entities and entity types corresponding to each identification entity;
the feature extraction module is used for carrying out vector conversion on each identification entity based on the entity type to obtain an initial entity feature vector of each identification entity; performing attention weighting on the initial entity feature vector based on a self-attention mechanism to obtain a target entity feature vector; matching and screening all the identification entities by utilizing the target entity feature vector to obtain a target entity;
and the medical record generating module is used for filling each target entity into a preset electronic medical record template based on the entity type to obtain the electronic medical record of the user.
Optionally, the entity recognition of the medical treatment text to obtain recognition entities and entity types corresponding to each recognition entity includes:
performing word segmentation on the medical text to obtain a plurality of word segmentation words, and converting the word segmentation words into vectors to obtain word segmentation word vectors;
extracting features of the word segmentation word vectors by using a BiLSTM model, and identifying and classifying the extracted features by using a pre-constructed classification function to obtain entity probabilities corresponding to preset field types;
determining word segmentation words corresponding to word segmentation word vectors with entity probability larger than a preset entity threshold value corresponding to the preset field type as entity words of the preset field type;
calculating the sequence coefficient of the entity words corresponding to each preset field type by using a serialization labeling algorithm, and combining all the entity words corresponding to the preset field types according to the sequence coefficient to obtain an initial recognition entity corresponding to the preset field type;
and carrying out entity alignment on the initial recognition entity to obtain a recognition entity corresponding to the initial recognition entity, and determining a preset field type corresponding to the initial recognition entity as the entity type of the recognition entity corresponding to the initial recognition entity.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one computer program; a kind of electronic device with high-pressure air-conditioning system
And the processor executes the computer program stored in the memory to realize the electronic medical record generation method.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned electronic medical record generating method.
The embodiment of the invention gathers the medical treatment dialogue text and the medical treatment inspection text to obtain medical treatment text, and carries out entity identification on the medical treatment text to obtain identification entities and entity types corresponding to each identification entity; the data of the data sources with different dimensions are summarized, so that the data of the user for medical treatment is more comprehensive, the result of entity identification is more comprehensive, the medical record of the user can be more accurately represented according to the electronic medical record generated by the result of entity identification, and the accuracy of electronic medical record generation is higher.
Drawings
FIG. 1 is a flowchart of a method for generating an electronic medical record according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of an electronic medical record generating device according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device for implementing a method for generating an electronic medical record according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a method for generating an electronic medical record. The execution main body of the electronic medical record generating method includes, but is not limited to, at least one of a server, a terminal and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the electronic medical record generating method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: the server can be an independent server, or can be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDNs), basic cloud computing services such as big data and artificial intelligent platforms, and the like.
Referring to fig. 1, which is a schematic flow chart of a method for generating an electronic medical record according to an embodiment of the present invention, in an embodiment of the present invention, the method for generating an electronic medical record includes the following steps:
s1, acquiring medical dialogue voice and diagnosis inspection data of a user for inquiring;
in the embodiment of the invention, the medical treatment dialogue voice is a consultation dialogue voice between a user and a doctor, and the medical treatment inspection data is structural data such as an inspection report of the user.
S2, performing text conversion on the medical treatment dialogue voice to obtain medical treatment dialogue text, and performing unstructured conversion on the medical treatment inspection data to obtain medical treatment inspection text;
in the embodiment of the invention, in order to better process the medical treatment data, different data are required to be converted into a data format convenient to process, so that the medical treatment dialogue voice is subjected to text conversion to obtain medical treatment dialogue text, and the medical treatment inspection data is subjected to unstructured conversion to obtain the medical treatment inspection text.
In detail, in the embodiment of the invention, the text in the diagnosis inspection data is extracted to realize unstructured conversion, so as to obtain the diagnosis inspection text.
S3, summarizing the medical treatment dialogue text and the medical treatment inspection text to obtain medical treatment text, and carrying out entity identification on the medical treatment text to obtain identification entities and entity types corresponding to each identification entity;
in the embodiment of the invention, in order to make the information of the medical treatment data of the user more comprehensive, the medical treatment dialogue text and the medical treatment checking text are summarized to obtain the medical treatment text.
Specifically, in the embodiment of the present invention, entity recognition is performed on the medical treatment text to obtain recognition entities and entity types corresponding to each recognition entity, including:
step A: performing word segmentation on the medical text to obtain a plurality of word segmentation words, and converting the word segmentation words into vectors to obtain word segmentation word vectors;
and (B) step (B): extracting features of the word segmentation word vectors by using a BiLSTM model, and identifying and classifying the extracted features by using a pre-constructed classification function to obtain entity probabilities corresponding to preset field types;
alternatively, the classification function in the embodiment of the present invention may be a softmax function.
Step C: determining word segmentation words corresponding to word segmentation word vectors with entity probability larger than a preset entity threshold value corresponding to the preset field type as entity words of the preset field type;
step D: calculating the sequence coefficient of the entity words corresponding to each preset field type by using a serialization labeling algorithm, and combining all the entity words corresponding to the preset field types according to the sequence coefficient to obtain an initial recognition entity corresponding to the preset field type;
because the entity words are only isolated words, in order to correctly combine the entity words, the order of the entity words needs to be determined, and therefore, the embodiment of the invention calculates the order labels of all the entity words corresponding to each preset field type by using the serialization labeling algorithm. The sequence label is a label for identifying the sequence of the entity words, the sequence labeling algorithm is utilized to calculate the sequence coefficient of each entity word, and all the entity words corresponding to the field type are combined according to the size of the sequence coefficient to obtain the initial identification entity corresponding to the field type. For example: the method comprises the steps that a preset field type is a symptom, the field type corresponds to two entity words, the sequence coefficient corresponding to a heart is 0.9, the sequence coefficient corresponding to a palpitation is 0.8, and then an initial recognition entity obtained by combining all entity words corresponding to the symptom field type according to the size of the sequence coefficient is palpitation; the sequence coefficients described in the embodiments of the present invention may also be identified by text, such as: and combining the corresponding entity words according to the sequence of text representation to obtain the initial recognition entity.
Step E: and carrying out entity alignment on the initial recognition entity to obtain a recognition entity corresponding to the initial recognition entity, and determining a preset field type corresponding to the initial recognition entity as the entity type of the recognition entity corresponding to the initial recognition entity.
In the embodiment of the invention, because the initial recognition entity is not the standard expression form of the entity, in order to enhance the uniformity of the electronic medical record, the entity alignment is carried out on the initial recognition entity by using a preset entity dictionary, so as to obtain the recognition entity corresponding to the initial recognition entity.
In another embodiment of the present invention, the medical care text may be stored in a blockchain node, and the high throughput characteristic of the blockchain node is utilized to improve the data access efficiency.
S4, carrying out vector conversion on each identification entity based on the entity type to obtain an initial entity feature vector of each identification entity;
in the embodiment of the present invention, vector conversion is performed on each of the identified entities based on the entity type to obtain an initial entity feature vector of each of the identified entities, including:
converting the identified entity into a vector to obtain an entity vector of the identified entity;
converting the entity type into a vector to obtain a type vector of the entity type;
and combining the entity vector of the identified entity with the type vector of the entity type corresponding to the identified entity to obtain the initial entity characteristic vector of the identified entity.
Specifically, in the embodiment of the present invention, converting the identified entity into a vector to obtain an entity vector of the identified entity includes:
converting each character in the recognition entity into a vector to obtain an entity character vector;
and combining all the entity character vectors according to the sequence of the corresponding characters in the recognition entity to obtain the entity vector.
In detail, in the embodiment of the present invention, converting the entity type into a vector to obtain a type vector of the entity type includes:
converting each character in the entity type into a vector to obtain an entity character vector;
and combining all the entity character vectors according to the sequence of the corresponding characters in the entity type to obtain the entity vector.
S5, carrying out attention weighting on the initial entity feature vector based on a self-attention mechanism to obtain a target entity feature vector;
in the embodiment of the invention, the needed characteristics in the initial entity characteristic vector are extracted, and the initial entity characteristic vector is weighted by attention based on a self-attention mechanism to obtain the target entity characteristic vector.
Specifically, the step S5 in the embodiment of the present invention includes:
acquiring a first query weight matrix, a first key weight matrix and a first value weight matrix in a first attention network constructed based on a self-attention mechanism;
the first query weight matrix, the first key weight matrix and the first value weight matrix in the first attention network in the embodiment of the present invention are weight parameters for mapping text feature vectors to K, Q, V in an attention mechanism.
Calculating by using the first query matrix and the first key matrix to obtain a first attention weight;
normalizing the first attention weight based on the vector dimension of the initial entity feature vector to obtain a first fusion weight;
and carrying out weighted calculation by using the first fusion weight and the first value matrix to obtain a target entity characteristic vector.
Specifically, in the embodiment of the present invention, the transpose of the first query matrix and the first key matrix are multiplied to obtain the first attention weight; dividing the arithmetic square root of the vector dimension of the first attention weight and the initial entity feature vector, and calculating a softmax function by taking an operation result as a variable parameter of the softmax function to obtain a first fusion weight; and multiplying the first fusion weight and the first value matrix to obtain the target entity feature vector.
S6, matching and screening all the identification entities by utilizing the target entity feature vector to obtain a target entity;
in order to screen the identification entities which can be filled in the electronic medical records, whether the identification entities are matched with the electronic medical records or not is measured, and the target entity feature vector is utilized to carry out matching screening on all the identification entities so as to obtain the target entity.
Specifically, the step S6 in the embodiment of the present invention includes:
performing feature compression on the target entity feature vector by using a pre-constructed full connection layer to obtain a compressed feature value;
optionally, in the embodiment of the present invention, there is only one node in the fully-connected layer.
And calculating a pre-constructed classification function by taking the compressed characteristic value as a function variable to obtain the matching probability of the target entity characteristic vector.
Specifically, in the embodiment of the present invention, the classification function is a sigmod function.
And screening all the identification entities by using the matching probability and a preset matching threshold value to obtain a target entity.
Specifically, in the embodiment of the invention, the matching probability larger than the matching threshold value is determined as the target matching probability; determining the compression characteristic value corresponding to the target matching probability as a target compression characteristic value; determining a target entity feature vector corresponding to the target compression feature value as an initial screening vector; determining an initial entity characteristic vector corresponding to the screening vector as a target screening vector; and determining the identification entity corresponding to the target screening vector as a target identification entity.
And S7, filling each target entity into a preset electronic medical record template based on the entity type to obtain the electronic medical record of the user.
In the embodiment of the invention, the electronic medical record template is a document of a preset type filled with basic information of a user.
Further, in the embodiment of the present invention, filling each target entity into a preset electronic medical record template based on the entity type to obtain an electronic medical record of the user, including:
acquiring the region type of each region in the electronic template;
consistency comparison is carried out on the entity types and the region types so as to determine the region corresponding to each entity type;
and filling all target entities corresponding to each entity type into a region corresponding to the entity type to obtain the electronic medical record.
Specifically, in the embodiment of the present invention, consistency comparison is performed on the entity type and the region type to determine a region corresponding to each entity type, including:
judging whether the entity type is the same as the region type;
and determining the region corresponding to the region type which is the same as the entity type as the region corresponding to the entity type.
As shown in fig. 2, a functional block diagram of the electronic medical record generating device according to the present invention is shown.
The electronic medical record generating device 100 of the present invention may be installed in an electronic apparatus. Depending on the implemented functions, the electronic medical record generating device may include an entity identification module 101, a feature extraction module 102, and a medical record generating module 103, where the modules may also be referred to as units, and refer to a series of computer program segments that can be executed by a processor of an electronic device and can perform fixed functions, and are stored in a memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the entity identification module 101 acquires medical dialogue voice and medical examination data of a user inquiry; performing text conversion on the medical treatment dialogue voice to obtain medical treatment dialogue text, and performing unstructured conversion on the medical treatment inspection data to obtain medical treatment inspection text; summarizing the medical treatment dialogue text and the medical treatment examination text to obtain medical treatment text, and carrying out entity identification on the medical treatment text to obtain identification entities and entity types corresponding to each identification entity;
the feature extraction module 102 performs vector conversion on each identified entity based on the entity type to obtain an initial entity feature vector of each identified entity; performing attention weighting on the initial entity feature vector based on a self-attention mechanism to obtain a target entity feature vector; matching and screening all the identification entities by utilizing the target entity feature vector to obtain a target entity;
the medical record generating module 103 fills each target entity into a preset electronic medical record template based on the entity type to obtain the electronic medical record of the user.
In detail, each module in the electronic medical record generating device 100 in the embodiment of the present invention adopts the same technical means as the electronic medical record generating method described in fig. 1 and can generate the same technical effects when in use, and will not be described herein.
Fig. 3 is a schematic structural diagram of an electronic device for implementing the electronic medical record generating method according to the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as an electronic medical record generating program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various data, such as codes of electronic medical record generation programs, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., electronic medical record generation programs, etc.) stored in the memory 11, and calling data stored in the memory 11.
The communication bus 12 may be a peripheral component interconnect standard (PerIPheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure classification circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
Optionally, the communication interface 13 may comprise a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The electronic medical record generating program stored in the memory 11 of the electronic device is a combination of a plurality of computer programs, which when run in the processor 10, can implement:
acquiring medical dialogue voice and diagnosis inspection data of a user;
performing text conversion on the medical treatment dialogue voice to obtain medical treatment dialogue text, and performing unstructured conversion on the medical treatment inspection data to obtain medical treatment inspection text;
summarizing the medical treatment dialogue text and the medical treatment examination text to obtain medical treatment text, and carrying out entity identification on the medical treatment text to obtain identification entities and entity types corresponding to each identification entity;
vector conversion is carried out on each identification entity based on the entity type, and an initial entity characteristic vector of each identification entity is obtained;
performing attention weighting on the initial entity feature vector based on a self-attention mechanism to obtain a target entity feature vector;
matching and screening all the identification entities by utilizing the target entity feature vector to obtain a target entity;
and filling each target entity into a preset electronic medical record template based on the entity type to obtain the electronic medical record of the user.
In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring medical dialogue voice and diagnosis inspection data of a user;
performing text conversion on the medical treatment dialogue voice to obtain medical treatment dialogue text, and performing unstructured conversion on the medical treatment inspection data to obtain medical treatment inspection text;
summarizing the medical treatment dialogue text and the medical treatment examination text to obtain medical treatment text, and carrying out entity identification on the medical treatment text to obtain identification entities and entity types corresponding to each identification entity;
vector conversion is carried out on each identification entity based on the entity type, and an initial entity characteristic vector of each identification entity is obtained;
performing attention weighting on the initial entity feature vector based on a self-attention mechanism to obtain a target entity feature vector;
matching and screening all the identification entities by utilizing the target entity feature vector to obtain a target entity;
and filling each target entity into a preset electronic medical record template based on the entity type to obtain the electronic medical record of the user.
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method for generating an electronic medical record, the method comprising:
acquiring medical dialogue voice and diagnosis inspection data of a user;
performing text conversion on the medical treatment dialogue voice to obtain medical treatment dialogue text, and performing unstructured conversion on the medical treatment inspection data to obtain medical treatment inspection text;
summarizing the medical treatment dialogue text and the medical treatment examination text to obtain medical treatment text, and carrying out entity identification on the medical treatment text to obtain identification entities and entity types corresponding to each identification entity;
vector conversion is carried out on each identification entity based on the entity type, and an initial entity characteristic vector of each identification entity is obtained;
performing attention weighting on the initial entity feature vector based on a self-attention mechanism to obtain a target entity feature vector;
matching and screening all the identification entities by utilizing the target entity feature vector to obtain a target entity;
and filling each target entity into a preset electronic medical record template based on the entity type to obtain the electronic medical record of the user.
2. The method of generating an electronic medical record according to claim 1, wherein the performing entity recognition on the medical treatment text to obtain recognition entities and entity types corresponding to each recognition entity includes:
performing word segmentation on the medical text to obtain a plurality of word segmentation words, and converting the word segmentation words into vectors to obtain word segmentation word vectors;
extracting features of the word segmentation word vectors by using a BiLSTM model, and identifying and classifying the extracted features by using a pre-constructed classification function to obtain entity probabilities corresponding to preset field types;
determining word segmentation words corresponding to word segmentation word vectors with entity probability larger than a preset entity threshold value corresponding to the preset field type as entity words of the preset field type;
calculating the sequence coefficient of the entity words corresponding to each preset field type by using a serialization labeling algorithm, and combining all the entity words corresponding to the preset field types according to the sequence coefficient to obtain an initial recognition entity corresponding to the preset field type;
and carrying out entity alignment on the initial recognition entity to obtain a recognition entity corresponding to the initial recognition entity, and determining a preset field type corresponding to the initial recognition entity as the entity type of the recognition entity corresponding to the initial recognition entity.
3. The electronic medical record generating method according to claim 1, wherein the performing vector conversion on each of the identified entities based on the entity type to obtain an initial entity feature vector of each of the identified entities includes:
converting the identified entity into a vector to obtain an entity vector of the identified entity;
converting the entity type into a vector to obtain a type vector of the entity type;
and combining the entity vector of the identified entity with the type vector of the entity type corresponding to the identified entity to obtain the initial entity characteristic vector of the identified entity.
4. The electronic medical record generating method as set forth in claim 3, wherein the converting the identified entity into a vector to obtain an entity vector of the identified entity includes:
converting each character in the recognition entity into a vector to obtain an entity character vector;
and combining all the entity character vectors according to the sequence of the corresponding characters in the recognition entity to obtain the entity vector.
5. The electronic medical record generating method according to claim 1, wherein the performing matching screening on all the identified entities by using the target entity feature vector to obtain a target entity includes:
performing feature compression on the target entity feature vector by using a pre-constructed full connection layer to obtain a compressed feature value;
and calculating a pre-constructed classification function by taking the compressed characteristic value as a function variable to obtain the matching probability of the target entity characteristic vector.
And screening all the identification entities by using the matching probability and a preset matching threshold value to obtain the target entity.
6. The electronic medical record generating method according to any one of claims 1 to 5, wherein the filling each target entity into a preset electronic medical record template based on the entity type to obtain the electronic medical record of the user includes:
acquiring the region type of each region in the electronic template;
consistency comparison is carried out on the entity types and the region types so as to determine the region corresponding to each entity type;
and filling all target entities corresponding to each entity type into a region corresponding to the entity type to obtain the electronic medical record.
7. An electronic medical record generating device, characterized by comprising:
the entity identification module is used for acquiring medical conversation voice and diagnosis inspection data of a user; performing text conversion on the medical treatment dialogue voice to obtain medical treatment dialogue text, and performing unstructured conversion on the medical treatment inspection data to obtain medical treatment inspection text; summarizing the medical treatment dialogue text and the medical treatment examination text to obtain medical treatment text, and carrying out entity identification on the medical treatment text to obtain identification entities and entity types corresponding to each identification entity;
the feature extraction module is used for carrying out vector conversion on each identification entity based on the entity type to obtain an initial entity feature vector of each identification entity; performing attention weighting on the initial entity feature vector based on a self-attention mechanism to obtain a target entity feature vector; matching and screening all the identification entities by utilizing the target entity feature vector to obtain a target entity;
and the medical record generating module is used for filling each target entity into a preset electronic medical record template based on the entity type to obtain the electronic medical record of the user.
8. The electronic medical record generating device as set forth in claim 7, wherein the performing entity recognition on the medical treatment text to obtain recognition entities and entity types corresponding to each recognition entity includes:
performing word segmentation on the medical text to obtain a plurality of word segmentation words, and converting the word segmentation words into vectors to obtain word segmentation word vectors;
extracting features of the word segmentation word vectors by using a BiLSTM model, and identifying and classifying the extracted features by using a pre-constructed classification function to obtain entity probabilities corresponding to preset field types;
determining word segmentation words corresponding to word segmentation word vectors with entity probability larger than a preset entity threshold value corresponding to the preset field type as entity words of the preset field type;
calculating the sequence coefficient of the entity words corresponding to each preset field type by using a serialization labeling algorithm, and combining all the entity words corresponding to the preset field types according to the sequence coefficient to obtain an initial recognition entity corresponding to the preset field type;
and carrying out entity alignment on the initial recognition entity to obtain a recognition entity corresponding to the initial recognition entity, and determining a preset field type corresponding to the initial recognition entity as the entity type of the recognition entity corresponding to the initial recognition entity.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the electronic medical record generation method of any one of claims 1 to 6.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the electronic medical record generation method according to any one of claims 1 to 6.
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