CN112599214A - Method and system for extracting ICD (interface control document) codes from electronic medical records - Google Patents
Method and system for extracting ICD (interface control document) codes from electronic medical records Download PDFInfo
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Abstract
The invention discloses a method and a system for extracting an ICD code from an electronic medical record, wherein the method for extracting the ICD code from the electronic medical record comprises the following steps: extracting medical entities in the electronic medical record based on an algorithm of deep learning sequence labeling and a preset rule, wherein the medical entities comprise one or more of disease diagnosis result data, examination data and operation data; and determining the ICD code corresponding to the medical entity based on a minimum edit distance algorithm. The method and the system for extracting the ICD codes from the electronic medical record can quickly extract the ICD codes for discharge settlement and help patients to quickly finish the discharge settlement.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a system for extracting an ICD code from an electronic medical record.
Background
ICD-9 is a surgical code, and ICD-10 is a disease code. Currently, the state requires the homepage of medical records to reflect which examinations and operations have been performed and to record the diagnosis result during the treatment process in the manner of ICD9 and ICD10 codes for regulating the electronic medical records in the medical industry. And simultaneously, the medical insurance bureau carries out reimbursement through the code record of the home page. It is important to accurately extract the diagnostic data, examination data, and surgical data (hereinafter referred to as medical entities) from the medical records and to map them to the correct ICD codes.
At present, most medical institutions manually fill in codes in a manual mode, workers need to search medical entities from dozens of or even hundreds of electronic medical records, and since medical entities written by doctors during electronic medical records do not completely conform to corresponding descriptions of the codes, errors, shorthand and the like may occur, and the workers can be in one-to-one correspondence only by using higher knowledge reserves. Meanwhile, a large number of patients are discharged and settled every day, and the mode of manually acquiring the ICD codes from the electronic medical records has the defects of high technical threshold, large workload, large amount of repeated work and the like.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention aims to provide a method and a system for extracting an ICD code from an electronic medical record, which can quickly extract the ICD code for discharge settlement.
In order to achieve the above object, the present invention provides a method for extracting an ICD code from an electronic medical record, which includes: extracting medical entities in the electronic medical record based on an algorithm of deep learning sequence labeling and a preset rule, wherein the medical entities comprise one or more of disease diagnosis result data, examination data and operation data; and determining the ICD code corresponding to the medical entity based on a minimum edit distance algorithm.
In an embodiment of the present invention, the extracting medical entities in an electronic medical record based on an algorithm labeled by a deep learning sequence and a preset rule includes: extracting comma or data after the semicolon in the diagnosis result of the medical record summary in the electronic medical record as a medical entity; and extracting the medical entity from the rest data of the electronic medical record based on an algorithm of deep learning sequence labeling.
In an embodiment of the present invention, the extracting medical entities in an electronic medical record based on an algorithm labeled by a deep learning sequence and a preset rule includes: and if a negative word exists in front of the medical entity in the electronic medical record obtained based on the algorithm of the deep learning sequence labeling, the medical entity is not extracted.
In an embodiment of the present invention, the extracting medical entities in an electronic medical record based on an algorithm labeled by a deep learning sequence and a preset rule includes: when medical entities are extracted through an algorithm of deep learning sequence labeling, the medical entities appearing in the medical history section of the electronic medical record are labeled in a distinguishing mode when data are labeled, and the medical entities labeled in the distinguishing mode are not extracted.
In an embodiment of the present invention, the extracting medical entities in an electronic medical record based on an algorithm labeled by a deep learning sequence and a preset rule includes: and if the medical entities in the electronic medical record obtained based on the algorithm of the deep learning sequence labeling are data of a medical history section, not extracting the medical entities of the medical history section.
In an embodiment of the present invention, the determining, based on the minimum edit distance algorithm, the ICD code corresponding to the medical entity includes: and respectively solving the similarity between each medical entity and the ICD code for each extracted medical entity based on a minimum edit distance algorithm, and normalizing the numerical value of a certain medical entity to be a certain ICD code if the similarity between the certain medical entity and the certain ICD code reaches a preset threshold value.
In an embodiment of the present invention, the method for extracting an ICD code from an electronic medical record further includes: after medical entities in the electronic medical records are extracted, the extracted medical entities are corrected based on the corresponding relation between the disease diagnosis result data and the examination data and the operation data, and error data are removed; and/or after determining the ICD code corresponding to the medical entity based on a minimum edit distance algorithm, performing proofreading on the ICD code based on the corresponding relation between the original data of the medical entity and the ICD code.
Based on the same inventive concept, the invention also provides a system for extracting the ICD code from the electronic medical record, which comprises the following steps: an entity extraction module and an ICD code determination module. The entity extraction module is used for extracting medical entities in the electronic medical record based on an algorithm of deep learning sequence labeling and a preset rule, wherein the medical entities comprise a plurality of disease diagnosis result data, examination data and operation data. The ICD code determining module is coupled with the entity extracting module and used for determining the ICD code corresponding to the medical entity based on a minimum edit distance algorithm.
Based on the same inventive concept, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for extracting an ICD code from an electronic medical record as described in any one of the above.
Based on the same inventive concept, the present invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method for extracting ICD codes from electronic medical records as described in any one of the above.
Compared with the prior art, according to the method and the system for extracting the ICD code from the electronic medical record, the medical entity is automatically extracted based on the sequence marking algorithm and the preset rule aiming at the unstructured electronic medical record information, and the similarity between the medical entity and the ICD code is determined based on the minimum edit distance algorithm, so that the ICD code information is quickly and accurately extracted from unstructured data and is subjected to normalization processing, the manual workload is greatly reduced, and the patient can be helped to quickly finish the discharge settlement.
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Fig. 1 is a method for extracting ICD codes from an electronic medical record according to an embodiment of the invention;
fig. 2 is a system for extracting ICD codes from electronic medical records according to an embodiment of the invention.
Detailed Description
The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
In order to extract an ICD code from an electronic medical record quickly and facilitate discharge and settlement of a patient, one embodiment provides a method for extracting an ICD code from an electronic medical record, as shown in fig. 1, the method includes: step S1 to step S2.
In step S1, medical entities in the electronic medical record are extracted based on the algorithm labeled by the deep learning sequence and preset rules, wherein the medical entities include a plurality of disease diagnosis result data, examination data and operation data.
Specifically, the preset rule may be set according to a rule on the electronic medical record. If the 'diagnosis result' in some cases is summarized, the patient can be distinguished by comma or semicolon which diagnosis is made. In this case, the extracting the medical entity in the electronic medical record based on the algorithm of the deep learning sequence labeling and the preset rule includes: extracting comma or data after the semicolon in the diagnosis result of the medical record summary in the electronic medical record as a medical entity; and extracting the medical entity from the rest data of the electronic medical record based on an algorithm of deep learning sequence labeling. This can further improve the efficiency of entity extraction using the rule.
For another example, the actual meaning of a certain operation or examination identified by the algorithm labeled by the deep learning sequence in the electronic medical record may be that the patient refuses to perform the operation or examination, and for this case, the extraction of the medical entity in the electronic medical record based on the algorithm labeled by the deep learning sequence and the preset rule includes: and if a negative word exists in front of the medical entity in the electronic medical record obtained based on the algorithm of the deep learning sequence labeling, the medical entity is not extracted.
As another example, the medical entity in the electronic medical record may be the patient's medical history data, not the result of the examination, and thus cannot be used for discharge settlement. For this case, two processing options are available. In a first processing mode, the extracting, based on the algorithm labeled by the deep learning sequence and the preset rule, the medical entity in the electronic medical record includes: and if the medical entities in the electronic medical record obtained based on the algorithm of the deep learning sequence labeling are data of a medical history section, not extracting the medical entities of the medical history section. In a second processing mode, the extracting the medical entity in the electronic medical record based on the algorithm labeled by the deep learning sequence and the preset rule includes: when medical entities are extracted through an algorithm of deep learning sequence labeling, the medical entities appearing in the medical history section of the electronic medical record are labeled differently when data are labeled, and the differentially labeled medical entities are not extracted.
In step S2, an ICD code corresponding to the medical entity is determined based on a minimum edit distance algorithm.
In particular, the extracted medical entities are normalized in this step, considering that the physician's writing in the electronic case may not match the ICD code. And respectively solving the similarity between each medical entity and the ICD code for each extracted medical entity based on a minimum edit distance algorithm, and normalizing the numerical value of a certain medical entity to be a certain ICD code if the similarity between the certain medical entity and the certain ICD code reaches a preset threshold value. The calculation of the editing distance refers to the minimum number of editing operations required for converting one phrase into another phrase. The allowed editing operations include: and replacing one character with another character, inserting one character or deleting one character, and calculating the similarity of the two phrases through times. When the similarity reaches a preset threshold, the extracted medical entity is considered to be a certain ICD-9 code or ICD-10 code.
Preferably, in order to improve the accuracy of the ICD extraction result, in an embodiment, the method for extracting an ICD code from an electronic medical record further includes: after medical entities in the electronic medical records are extracted, the extracted medical entities are corrected based on the corresponding relation between the disease diagnosis result data and the examination data and the operation data, and error data are removed. Specifically, the correspondence between each item of disease diagnosis result data and examination data and operation data is collected in advance, and whether or not the diagnosis result corresponds to the examination and the operation in the extracted medical entity is checked based on the correspondence.
Preferably, in order to improve the accuracy of the ICD extraction result, in an embodiment, the method for extracting an ICD code from an electronic medical record further includes: after determining the ICD code corresponding to the medical entity based on a minimum edit distance algorithm, correcting the ICD code based on the corresponding relation between the original data of the medical entity and the ICD code.
Based on the same inventive concept, an embodiment further provides a system for extracting an ICD code from an electronic medical record, as shown in fig. 2, which includes: an entity extraction module 10 and an ICD code determination module 11.
The entity extraction module 10 is configured to extract medical entities in the electronic medical record based on an algorithm of deep learning sequence labeling and preset rules, where the medical entities include a plurality of disease diagnosis result data, examination data, and surgical data.
Specifically, the preset rule may be set according to a rule on the electronic medical record. If the 'diagnosis result' in some cases is summarized, the patient can be distinguished by comma or semicolon which diagnosis is made. In this case, the extraction module is configured to extract, as a medical entity, comma or data after a semicolon in the diagnosis result of the medical record summary in the electronic medical record; and extracting the medical entity from the rest data of the electronic medical record based on an algorithm of deep learning sequence labeling. This can further improve the efficiency of entity extraction using the rule.
As another example, the true meaning of a certain operation or examination identified by an algorithm labeled with a deep-learning sequence in an electronic medical record may be that the patient refuses to do the operation or examination, for which case the extraction module is further configured to: and if a negative word exists in front of the medical entity in the electronic medical record obtained based on the algorithm of the deep learning sequence labeling, the medical entity is not extracted.
As another example, the medical entity in the electronic medical record may be the patient's medical history data, not the result of the examination, and thus cannot be used for discharge settlement. For this case, two processing options are available. In a first processing mode, the extraction module is configured to not extract the medical entity in the medical history section if the medical entity in the electronic medical record obtained based on the algorithm labeled by the deep learning sequence is data of the medical history section. In a second processing mode, the extraction module is configured to, when extracting medical entities through an algorithm labeled by a deep learning sequence, perform a distinguishing labeling on the medical entities appearing in the medical history section of the electronic medical record when labeling data, and not perform extraction on the medical entities with the distinguishing labeling.
The ICD code determining module 11 is coupled to the entity extracting module 10 and configured to determine an ICD code corresponding to the medical entity based on a minimum edit distance algorithm. The ICD code determining module 11 is specifically configured to, for each extracted medical entity, respectively obtain similarity between each medical entity and an ICD code based on a minimum edit distance algorithm, and normalize a numerical value of a certain medical entity to a certain ICD code if the similarity between the certain medical entity and the certain ICD code reaches a preset threshold.
Preferably, in order to improve the accuracy of the ICD extraction result, in an embodiment, the entity extraction module 10 is further configured to: after medical entities in the electronic medical records are extracted, the extracted medical entities are corrected based on the corresponding relation between the disease diagnosis result data and the examination data and the operation data, and error data are removed. Specifically, the correspondence between each item of disease diagnosis result data and examination data and operation data is collected in advance, and whether or not the diagnosis result corresponds to the examination and the operation in the extracted medical entity is checked based on the correspondence.
Preferably, in order to improve the accuracy of the ICD extraction result, in an embodiment, the ICD code determining module 11 is further configured to: after determining the ICD code corresponding to the medical entity based on a minimum edit distance algorithm, correcting the ICD code based on the corresponding relation between the original data of the medical entity and the ICD code.
Based on the same inventive concept, an embodiment further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the steps of the method for extracting the ICD code from the electronic medical record as described in any one of the above.
Based on the same inventive concept, an embodiment also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method for extracting an ICD code from an electronic medical record as described in any one of the above.
In summary, according to the method and system for extracting the ICD codes from the electronic medical record in the embodiment, the medical entity is automatically extracted based on the sequence labeling algorithm and the preset rule for the unstructured electronic medical record information, and the similarity between the medical entity and the ICD codes is determined based on the minimum edit distance algorithm, so that the ICD code information is quickly and accurately extracted from the unstructured data and is normalized, the manual workload is greatly reduced, and the patient can be helped to quickly complete the discharge settlement.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable one skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims and their equivalents.
Claims (10)
1. A method for extracting an ICD code from an electronic medical record is characterized by comprising the following steps:
extracting medical entities in the electronic medical record based on an algorithm of deep learning sequence labeling and a preset rule, wherein the medical entities comprise one or more of disease diagnosis result data, examination data and operation data; and
and determining the ICD code corresponding to the medical entity based on a minimum edit distance algorithm.
2. The method as claimed in claim 1, wherein the extracting the ICD code from the electronic medical record based on the algorithm of deep learning sequence labeling and the preset rule comprises:
extracting comma or data after the semicolon in the diagnosis result of the medical record summary in the electronic medical record as a medical entity;
and extracting the medical entity from the rest data of the electronic medical record based on an algorithm of deep learning sequence labeling.
3. The method as claimed in claim 1, wherein the extracting the ICD code from the electronic medical record based on the algorithm of deep learning sequence labeling and the preset rule comprises:
and if a negative word exists in front of the medical entity in the electronic medical record obtained based on the algorithm of the deep learning sequence labeling, the medical entity is not extracted.
4. The method as claimed in claim 1, wherein the extracting the ICD code from the electronic medical record based on the algorithm of deep learning sequence labeling and the preset rule comprises:
when medical entities are extracted through an algorithm of deep learning sequence labeling, the medical entities appearing in the medical history section of the electronic medical record are labeled in a distinguishing mode when data are labeled, and the medical entities labeled in the distinguishing mode are not extracted.
5. The method as claimed in claim 1, wherein the extracting the ICD code from the electronic medical record based on the algorithm of deep learning sequence labeling and the preset rule comprises:
and if the medical entities in the electronic medical record obtained based on the algorithm of the deep learning sequence labeling are data of a medical history section, not extracting the medical entities of the medical history section.
6. The method of claim 1, wherein the determining the ICD code corresponding to the medical entity based on the minimum edit distance algorithm comprises:
and respectively solving the similarity between each medical entity and the ICD code for each extracted medical entity based on a minimum edit distance algorithm, and normalizing the numerical value of a certain medical entity to be a certain ICD code if the similarity between the certain medical entity and the certain ICD code reaches a preset threshold value.
7. The method for extracting an ICD code from an electronic medical record as claimed in claim 1, wherein the method for extracting an ICD code from an electronic medical record further comprises:
after medical entities in the electronic medical records are extracted, the extracted medical entities are corrected based on the corresponding relation between the disease diagnosis result data and the examination data and the operation data, and error data are removed; and/or
After determining the ICD code corresponding to the medical entity based on a minimum edit distance algorithm, correcting the ICD code based on the corresponding relation between the original data of the medical entity and the ICD code.
8. A system for extracting ICD codes from an electronic medical record, comprising:
the entity extraction module is used for extracting medical entities in the electronic medical record based on an algorithm of deep learning sequence labeling and a preset rule, wherein the medical entities comprise a plurality of disease diagnosis result data, examination data and operation data; and
and the ICD code determining module is coupled with the entity extracting module and used for determining the ICD code corresponding to the medical entity based on a minimum edit distance algorithm.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for extracting ICD codes from an electronic medical record as claimed in any one of claims 1 to 7.
10. A non-transitory computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method for extracting ICD codes from electronic medical records according to any one of claims 1 to 7.
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Application publication date: 20210402 |