CN113496124A - Semantic analysis method and device for medical document, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a semantic analysis method and device for a medical document, electronic equipment and a storage medium, and relates to the technical field of text analysis. The method comprises obtaining a medical document; performing word segmentation and labeling processing on the medical document by using a medical term library with term classification parameters to obtain the medical document labeled by the term classification parameters; and matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain the target medical document with each sentence matched with the medical document sentence pattern, so that the target medical document has a specific grammatical structure and medical semantics. The semantic analysis method, the semantic analysis device, the electronic equipment and the storage medium for the medical document can accurately analyze the medical semantics of the sentences in the medical document, eliminate ambiguity and meet the medical application requirements.
Description
Technical Field
The present application relates to the field of text analysis technologies, and in particular, to a method and an apparatus for semantic analysis of a medical document, an electronic device, and a storage medium.
Background
At present, semantic analysis of sentences in medical documents such as electronic medical records generally adopts a natural language processing mode, and the processing of clinical information in a Chinese environment includes term segmentation, part of speech tagging, entity identification and the like of medical texts.
However, since the medical document is a professional document used in a medical environment, the style and structure of the medical document focuses more on the rationality of clinical medical application, and is not limited to the expression of simple natural language, for example, the electronic medical record may include chief complaints, current medical history, past medical history, physical signs, etc., when describing these terms, a very simple manner is often adopted, and the terms do not form the complete structure of the main object of the natural language, so in some specific contexts, the medical document sentence may not completely correspond to the natural language, and one term or phrase may form one sentence, so that the processing result may not obtain accurate medical semantics, and may not meet the medical application requirements, and may even generate many spurious.
Therefore, how to provide the most effective scheme for accurately analyzing the semantics in the medical document has become an urgent problem in the prior art.
Disclosure of Invention
In a first aspect, an embodiment of the present application provides a semantic analysis method for a medical document, including:
acquiring a medical document;
performing word segmentation and labeling processing on the medical document by using a medical term library with term classification parameters to obtain the medical document labeled by the term classification parameters;
matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain target medical documents of which each sentence is matched with the medical document sentence pattern, so that the target medical documents have specific grammatical structures and medical semantics;
wherein, the term concept and the classification parameter of each medical term are stored in the medical term library; the medical document sentence pattern library comprises a plurality of medical document sentence patterns, the medical document sentence patterns comprise at least one medical information unit, and the medical information units comprise classification parameters and semantic labels of medical terms.
In one possible design, the method further includes:
identifying the medical activity object in the target medical document according to the medical information unit in the sentence pattern; and simultaneously recording the medical activity object link in the target medical document to a medical activity object database.
In one possible design, each term concept corresponds to 1 to a plurality of medical terms in the medical term library, and each medical term corresponds to 1 term concept, and the method further includes:
updating the concept of the target medical term in the medical term library when the semantic label of the target medical term in the medical document sentence pattern library is inconsistent with the concept of the target medical term in the medical term library;
wherein the target medical term is a medical term in the medical document.
In one possible design, each term concept corresponds to 1 to a plurality of medical terms in the medical term library, and each medical term corresponds to 1 term concept, and the method further includes:
when a term concept related to a target medical term or a target medical term in the medical term library is missing;
adding the target medical term or term concept in the medical term library according to the term concept about the target medical term in the medical document sentence pattern library;
wherein the target medical term is a medical term in the medical document.
In one possible design, the acquiring the medical document includes:
acquiring an original medical document uploaded by a user;
and converting the original medical document into a text format to obtain the medical document in the text format.
In one possible design, the method further includes:
and establishing the medical term library and the medical document sentence pattern library.
In one possible design, the medical term library also records terms associated with the medical term.
In a second aspect, an embodiment of the present application provides a semantic analysis apparatus for a medical document, including:
the acquisition module is used for acquiring the medical documents;
the word segmentation and labeling module is used for applying a medical term library with term classification parameters to perform word segmentation and labeling processing on the medical document to obtain the medical document labeled by the term classification parameters;
the sentence pattern matching module is used for matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain target medical documents of which each sentence is matched with the medical document sentence pattern, so that the target medical documents have specific grammatical structures and medical semantics;
wherein, the term concept and the classification parameter of each medical term are stored in the medical term library; the medical document sentence pattern library comprises a plurality of medical document sentence patterns, the medical document sentence patterns comprise at least one medical information unit, and the medical information units comprise classification parameters and semantic labels of medical terms.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete mutual communication through the bus;
a memory for storing a computer program;
the processor is used for executing the program stored in the memory and realizing the following processes:
acquiring a medical document;
performing word segmentation and labeling processing on the medical document by using a medical term library with term classification parameters to obtain the medical document labeled by the term classification parameters;
matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain target medical documents of which each sentence is matched with the medical document sentence pattern, so that the target medical documents have specific grammatical structures and medical semantics;
the medical term library is recorded with term classification parameters of a plurality of medical terms, the medical document sentence pattern library comprises a plurality of medical document sentence patterns, the medical document sentence patterns comprise at least one medical information unit, and the medical information unit comprises semantic labels of the medical terms.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the following processes:
acquiring a medical document;
performing word segmentation and labeling processing on the medical document by using a medical term library with term classification parameters to obtain the medical document labeled by the term classification parameters;
matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain target medical documents of which each sentence is matched with the medical document sentence pattern, so that the target medical documents have specific grammatical structures and medical semantics;
the medical term library is recorded with term classification parameters of a plurality of medical terms, the medical document sentence pattern library comprises a plurality of medical document sentence patterns, the medical document sentence patterns comprise at least one medical information unit, and the medical information unit comprises semantic labels of the medical terms.
The above-mentioned at least one technical scheme that this application one or more embodiments adopted can reach following beneficial effect:
the medical treatment document is subjected to word segmentation and labeling processing through the medical term library to obtain the medical treatment document labeled by term classification parameters, and the labeled medical treatment document sentences are matched with the medical treatment document sentence patterns in the medical treatment document sentence pattern library to obtain the target medical treatment document of which each sentence is matched with the medical treatment document sentence pattern, so that the target medical treatment document has a specific grammatical structure and medical semantics, the medical semantics of the sentences in the medical treatment document can be accurately analyzed, ambiguity is eliminated, and the medical application requirements are met.
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The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure in any way. In the drawings:
fig. 1 is a flowchart of a semantic analysis method for a medical document according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a semantic analysis device for a medical document according to an embodiment of the present application.
Detailed Description
In order to accurately analyze the semantics in the medical document, the embodiment of the application provides a semantic analysis method, a semantic analysis device, electronic equipment and a storage medium for the medical document.
The semantic analysis method of the medical document provided by the embodiment of the application can be used for a user terminal or a server, and the user terminal can be, but is not limited to, a personal computer, a smart phone, a tablet computer, a Personal Digital Assistant (PDA), and the like.
The following will describe in detail the semantic analysis method of the medical document provided in the embodiment of the present application.
The embodiment of the application provides a semantic analysis method of a medical document, which is used for semantic analysis of the medical document. As shown in fig. 1, the semantic analysis method for a medical document provided in the embodiment of the present application may include the following steps:
step S101, acquiring a medical document.
In the embodiment of the application, the medical document can be an electronic medical record, a physical examination report and the like. When the semantic analysis of the medical document is performed, the original medical document can be uploaded by a user, and then the original medical document is converted into a text format, so that the medical document in the text format is obtained, and the subsequent analysis and processing are facilitated.
Step S102, the medical term library with the term classification parameters is applied to carry out word segmentation and labeling processing on the medical documents, and the medical documents labeled by the term classification parameters are obtained.
In the embodiment of the present application, a medical term library is pre-established, and a large number of medical terms and term parameters of the medical terms are recorded in the medical term library, and the term parameters include, but are not limited to, term classification parameters of the medical terms, term concepts of the medical terms, and associated terms.
The term classification parameter of the medical term may be part of speech of the term, such as a body word, a class predicate, a class particle, a pronoun, an exclamation word, and the like. The term concept of the medical term may be a clear, formalized, shareable definition of the concept to which the term refers. The associated terms may be terms pertaining to an inclusive relationship, an affiliation relationship, an equivalence relationship with the medical terms.
When the medical term library is established, the medical term library can be established according to, but not limited to, WS data metadata set standard, ICD international classification standard, observation index identification logic naming and coding system LOINC, medical system nomenclature-clinical terms SNOMED CT, OMAHA term set, and clinical practical application. When the medical term library is established, the selection should be performed according to the field of the medical document to be processed, so as to ensure that the term content covers the field of the medical document.
When performing the word segmentation process, the word segmentation process is performed on each medical document sentence in the medical document through Natural Language Processing (NLP) and a medical term library, and the medical term in each medical document sentence in the medical document is identified. And then, labeling the term classification parameters for the medical terms in the medical document sentences of the medical document according to the medical term library, thereby obtaining the medical document labeled with the term classification parameters.
Further, in the embodiment of the present application, when labeling, a term concept, an associated term, and the like may be further labeled to the medical term in each medical document sentence of the medical document.
And step S103, matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain target medical documents of which each sentence is matched with the medical document sentence pattern, so that the target medical documents have specific grammatical structures and medical semantics.
In the embodiment of the application, a medical document sentence pattern library is also pre-established, a large number of medical document sentence patterns are recorded in the medical document sentence pattern library, and the medical document sentence patterns comprise trigger words and at least one medical information unit. When there are a plurality of medical information units, the plurality of medical information units may form a fixed combination, i.e. an information unit group.
The medical information unit comprises medical terms, semantic labels of the medical terms, classification parameters of the medical terms and the like. The trigger may be a lead or a conjunction, etc. for leading or linking medical terms in the medical information unit, e.g. the trigger may be "cause", "therefore", "under … …", etc. The trigger and the medical information unit can be defined as an ordered or unordered structure, and the repetition times of the trigger and the medical information unit are limited by the repetition marks.
The sentence pattern structure is an organization structure pattern of a sentence, and is an organization rule of terms and symbols constituting the sentence. For example, in one embodiment, a statement of the type "subject + predicate" may be referred to as a sentence structure, and a statement of the type "subject + verb + table" may also be referred to as a sentence structure.
When matching, the medical document sentence pattern structure matched with the medical document sentence pattern structure is matched from the medical document sentence pattern library according to the sentence pattern structure, the classification parameters are consistent with the term classification parameters of the medical term in the medical document sentence, and the medical document sentence pattern matched with the terms is obtained, so that the target medical document of which each sentence is matched with the medical document sentence pattern is obtained. Meanwhile, according to the semantic labels of the medical terms in the matched sentence pattern of the medical document, corresponding semantic labels are added to the medical terms in the target medical document, so that the target medical document has a specific grammatical structure and medical semantics.
For example, in one embodiment, a sentence pattern structure of a medical document sentence a is "subject + system verb + table language", a sentence pattern structure of "subject + system verb + table language" medical document sentence pattern B exists in the medical document sentence pattern library, the classification parameter of each medical term in the medical document sentence pattern B is the same as the term classification parameter of each medical term in the medical document sentence a in a one-to-one correspondence, and the medical term in the medical document sentence pattern B is the same as or associated with the medical term in the medical document sentence a in a one-to-one correspondence, so that the medical document sentence pattern B may be considered as matching the medical document sentence a, and the semantic meaning of the medical document sentence a is obtained according to the semantic label of each medical term in the medical document sentence pattern B.
Further, in this embodiment of the present application, when the semantic tag related to the target medical term in the medical document sentence pattern library is inconsistent with the term concept related to the target medical term in the medical term library, the term concept related to the target medical term in the medical term library may be updated according to the semantic tag related to the target medical term in the medical document sentence pattern library. Wherein the target medical term is one or more medical terms in the medical document. In this way, erroneous term concepts of the medical terms in the medical term base can be automatically corrected.
Further, in the embodiment of the present application, when a term concept related to a target medical term in a medical term library is missing, a term concept related to the target medical term may be further added to the medical term library according to a semantic tag related to the target medical term in a medical document sentence pattern library. Wherein the target medical term is one or more medical terms in the medical document. Thus, new medical terms can be found and the medical term in the medical term library can be automatically updated.
In addition, in the embodiment of the present application, the medical activity object in the target medical document may also be identified according to the medical information unit in the sentence pattern, and the medical activity object may be, but is not limited to, a doctor, a nurse, a medicine, a disease, and the like. Upon identifying a medical activity object in the target medical document, the medical activity object link in the target medical document may be recorded to a medical activity object database. In this manner, structured data can be transformed into computer-recognizable data for subsequent use in analytical processing of the data.
To sum up, the semantic analysis method for medical documents provided in the embodiments of the present application performs word segmentation and labeling processing on medical documents through a medical term library with term classification parameters to obtain medical documents labeled with the term classification parameters, matches the labeled medical document sentences with the medical document sentence patterns in a medical document sentence pattern library to obtain target medical documents with each sentence matched with the medical document sentence pattern, and adds corresponding semantic tags to the medical terms in the target medical documents according to the semantic tags of the medical terms in the matched medical document sentence patterns, so that the target medical documents have specific grammatical structures and medical semantics, and thus can accurately find out the medical semantics of the sentences in the medical documents, eliminate ambiguity, and meet medical application requirements. Meanwhile, when the term concept of the medical term in the medical term library is wrong, the term concept of the medical term in the medical term library can be automatically corrected. Secondly, new medical terms can be discovered and the medical term in the medical term library can be automatically updated. In addition, the medical activity object in the target medical document can be identified and recorded into the medical activity object database, so that the medical activity object in the target medical document can be converted into structured data which can be identified by a computer for the subsequent analysis and processing of the data.
Fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 2, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 2, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the semantic analysis device of the medical document on a logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
acquiring a medical document;
performing word segmentation and labeling processing on the medical document by using a medical term library with term classification parameters to obtain the medical document labeled by the term classification parameters;
matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain target medical documents of which each sentence is matched with the medical document sentence pattern, so that the target medical documents have specific grammatical structures and medical semantics;
wherein, the term concept and the classification parameter of each medical term are stored in the medical term library; the medical document sentence pattern library comprises a plurality of medical document sentence patterns, the medical document sentence patterns comprise at least one medical information unit, and the medical information units comprise classification parameters and semantic labels of medical terms.
The method executed by the semantic analysis device of the medical document disclosed in the embodiment of fig. 2 of the present application can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in one or more embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with one or more embodiments of the present application may be embodied directly in the hardware decoding processor, or in a combination of the hardware and software modules included in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The electronic device may also execute the method of fig. 1 and implement the functions of the semantic analysis apparatus for medical documents in the embodiment shown in fig. 2, which are not described herein again in this application embodiment.
Of course, besides the software implementation, the electronic device of the present application does not exclude other implementations, such as a logic device or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.
Embodiments of the present application also provide a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to perform the method of the embodiment shown in fig. 1, and are specifically configured to:
acquiring a medical document;
performing word segmentation and labeling processing on the medical document by using a medical term library with term classification parameters to obtain the medical document labeled by the term classification parameters;
matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain target medical documents of which each sentence is matched with the medical document sentence pattern, so that the target medical documents have specific grammatical structures and medical semantics;
wherein, the term concept and the classification parameter of each medical term are stored in the medical term library; the medical document sentence pattern library comprises a plurality of medical document sentence patterns, the medical document sentence patterns comprise at least one medical information unit, and the medical information units comprise classification parameters and semantic labels of medical terms.
Fig. 3 is a schematic structural diagram of a semantic analysis device for a medical document according to an embodiment of the present application. Referring to fig. 3, in one software implementation, the semantic analysis apparatus for medical documents includes:
the acquisition module is used for acquiring the medical documents;
the word segmentation and labeling module is used for applying a medical term library with term classification parameters to perform word segmentation and labeling processing on the medical document to obtain the medical document labeled by the term classification parameters;
the sentence pattern matching module is used for matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain target medical documents of which each sentence is matched with the medical document sentence pattern, so that the target medical documents have specific grammatical structures and medical semantics;
wherein, the term concept and the classification parameter of each medical term are stored in the medical term library; the medical document sentence pattern library comprises a plurality of medical document sentence patterns, the medical document sentence patterns comprise at least one medical information unit, and the medical information units comprise classification parameters and semantic labels of medical terms.
In short, the above description is only a preferred embodiment of this document, and is not intended to limit the scope of protection of this document. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document shall be included in the protection scope of this document.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
All the embodiments in this document are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Claims (10)
1. A method for semantic analysis of a medical document, comprising:
acquiring a medical document;
performing word segmentation and labeling processing on the medical document by using a medical term library with term classification parameters to obtain the medical document labeled by the term classification parameters;
matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain target medical documents of which each sentence is matched with the medical document sentence pattern, so that the target medical documents have specific grammatical structures and medical semantics;
wherein, the term concept and the classification parameter of each medical term are stored in the medical term library; the medical document sentence pattern library comprises a plurality of medical document sentence patterns, the medical document sentence patterns comprise at least one medical information unit, and the medical information units comprise classification parameters and semantic labels of medical terms.
2. The method of claim 1, further comprising:
identifying the medical activity object in the target medical document according to the medical information unit in the sentence pattern; and simultaneously recording the medical activity object link in the target medical document to a medical activity object database.
3. The method of claim 1, wherein each term concept corresponds to 1 to a plurality of medical terms in the medical term library, and each medical term corresponds to 1 term concept, the method further comprising:
updating the concept of the target medical term in the medical term library when the semantic label of the target medical term in the medical document sentence pattern library is inconsistent with the concept of the target medical term in the medical term library;
wherein the target medical term is a medical term in the medical document.
4. The method of claim 1, wherein each term concept corresponds to 1 to a plurality of medical terms in the medical term library, and each medical term corresponds to 1 term concept, the method further comprising:
when a term concept related to a target medical term or a target medical term in the medical term library is missing;
adding the target medical term or term concept in the medical term library according to the term concept about the target medical term in the medical document sentence pattern library;
wherein the target medical term is a medical term in the medical document.
5. The method of claim 1, wherein the obtaining a medical document comprises:
acquiring an original medical document uploaded by a user;
and converting the original medical document into a text format to obtain the medical document in the text format.
6. The method of claim 1, wherein prior to obtaining the medical document, the method further comprises:
and establishing the medical term library and the medical document sentence pattern library.
7. The method of claim 1, wherein the medical term library further records associated terms of medical terms.
8. A semantic analysis apparatus for a medical document, comprising:
the acquisition module is used for acquiring the medical documents;
the word segmentation and labeling module is used for applying a medical term library with term classification parameters to perform word segmentation and labeling processing on the medical document to obtain the medical document labeled by the term classification parameters;
the sentence pattern matching module is used for matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain target medical documents of which each sentence is matched with the medical document sentence pattern, so that the target medical documents have specific grammatical structures and medical semantics;
wherein, the term concept and the classification parameter of each medical term are stored in the medical term library; the medical document sentence pattern library comprises a plurality of medical document sentence patterns, the medical document sentence patterns comprise at least one medical information unit, and the medical information units comprise classification parameters and semantic labels of medical terms.
9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing the communication between the processor and the memory through the bus;
a memory for storing a computer program;
the processor is used for executing the program stored in the memory and realizing the following processes:
acquiring a medical document;
performing word segmentation and labeling processing on the medical document by using a medical term library with term classification parameters to obtain the medical document labeled by the term classification parameters;
matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain target medical documents of which each sentence is matched with the medical document sentence pattern, so that the target medical documents have specific grammatical structures and medical semantics;
wherein, the term concept and the classification parameter of each medical term are stored in the medical term library; the medical document sentence pattern library comprises a plurality of medical document sentence patterns, the medical document sentence patterns comprise at least one medical information unit, and the medical information units comprise classification parameters and semantic labels of medical terms.
10. A computer-readable storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the following procedure:
acquiring a medical document;
performing word segmentation and labeling processing on the medical document by using a medical term library with term classification parameters to obtain the medical document labeled by the term classification parameters;
matching the marked medical document sentences with the medical document sentence patterns in the medical document sentence pattern library to obtain target medical documents of which each sentence is matched with the medical document sentence pattern, so that the target medical documents have specific grammatical structures and medical semantics;
wherein, the term concept and the classification parameter of each medical term are stored in the medical term library; the medical document sentence pattern library comprises a plurality of medical document sentence patterns, the medical document sentence patterns comprise at least one medical information unit, and the medical information units comprise classification parameters and semantic labels of medical terms.
Priority Applications (1)
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