CN115964472A - ICD coding method, ICD coding query method, coding system and query system - Google Patents
ICD coding method, ICD coding query method, coding system and query system Download PDFInfo
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Abstract
The invention discloses an ICD coding method, an ICD coding query method, a coding system and a query system, which relate to the technical field of information retrieval, wherein the ICD coding method comprises the following steps: obtaining a set of medical terms, the set of medical terms including a disease name; establishing an ontology model according to the disease name and the normalization name thereof; establishing a mapping relation between the normalization name and the concept code; and establishing a mapping relation between the concept codes and the ICD codes. Semantically normalizing the disease names through the ontology model so as to solve the problem of text difference of disease name expression and improve the accuracy of ICD coding; through the mapping of the normalized name and the concept code and the mapping of the concept code and the ICD code, even the ICD codes of different versions or regions can obtain effective mapping so as to adapt to the ICD codes of different versions and improve the applicability and the reusability.
Description
Technical Field
The invention relates to the technical field of information retrieval, in particular to an ICD coding method, an ICD coding query method, a coding system and a query system.
Background
As an important component of International Statistical Classification standards and Health information standards systems for Diseases and Related Health Problems, international Statistical Classification of Diseases and Related Health Problems (ICD) is widely applied to clinical research, medical outcome monitoring, health care management, health resource allocation, and the like, and has a profound and wide impact on medical Health service systems.
The currently mainstream application version is ICD-10, which plays an important role in standardizing disease classification and promoting information exchange, but the problems of internal and external code phenomena and the like are generally caused by the inconsistent ICD coding versions applied in provinces and cities, lack of an integral management mechanism in localized maintenance and updating, large coding difference among various expansion versions and difficulty in mutual exchange and insufficient clinical applicability. The unified data coding is the premise of medical big data analysis, the foundation of wide application of the medical big data is laid, and the problems of inconsistent coding versions, inaccurate coding and the like are one of the important reasons for retarding the unified analysis and application of the health medical big data.
In the prior art, a full-text retrieval matching system based on an ICD9/10 word segmentation word bank is divided into a data collection module, a data analysis module, an index configuration and timing task module and a matching engine external service module, and matched ICD9/10 codes and names are returned by calling an Elasticissearch search engine to index the ICD9/10 word segmentation word bank. The method can recommend ICD codes to one extent, but cannot process matching between texts outside a word segmentation word bank and codes, cannot effectively solve the problem of diagnostic code matching with consistent semantics but larger text differences, and cannot effectively solve the problem of difficult coding standardization caused by ICD code version differences.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides an ICD coding method, an ICD coding query method, a coding system and a query system, which overcome the problem of diversified disease name expressions through an ontology model, are easy to code and are suitable for ICD codes of different versions or multiple versions.
The invention discloses an ICD coding method, which comprises the following steps: obtaining a set of medical terms, the set of medical terms including a disease name; establishing an ontology model according to the disease name and the normalization name thereof; establishing a mapping relation between the normalization name and the concept code; and establishing a mapping relation between the concept codes and the ICD codes.
Preferably, the set of medical terms includes terms in any one of the following fields or combinations thereof:
diseases, symptoms, anatomy, surgery, biology, medicine, medical devices, examinations, imaging, nursing, genetic, and spoken language;
the ICD code comprises any one of the following coding systems or a combination thereof:
ICD-10, ICD-9-CM-3, ICD-11, and ICD-O.
Preferably, the method for establishing the ontology model comprises the following steps:
determining professional fields and categories; examining the possibility of reusing the existing ontology; listing a medical term list;
defining classes and class hierarchies; defining attributes of classes, wherein the attributes of the classes comprise intrinsic attributes, extrinsic attributes and relations with other classes, the intrinsic attributes are used for describing occurrence parts and morphological changes of diseases, and the extrinsic attributes are used for describing treatment methods; defining a facet of the attribute, wherein the facet comprises a value type, a domain and a value range; an instance is created.
Preferably, the method for ontology model optimization comprises the following steps:
determining an ontology model for subdividing the professional field;
fusing the plurality of ontology models according to an ontology integration method to obtain a fused ontology model;
and continuously optimizing and correcting the fusion ontology model.
The invention also provides a coding system for realizing the ICD coding method, which comprises a medical term set, an ontology model construction module and a mapping construction module; the set of medical terms includes a disease name; the ontology model building module is used for building an ontology model according to the disease name and the normalized name thereof; the mapping construction module is used for establishing the mapping relation between the normalization name and the concept code according to the medical term set and the ICD code library; and establishing a mapping relation between the concept codes and the ICD codes.
The invention also provides an ICD coding query method, which comprises the following steps: acquiring a disease query statement; acquiring an ontology model, wherein the ontology model is used for acquiring a normalized name of a disease according to the disease name; matching the disease query statement with the normalization name or the disease name in the ontology model to obtain a corresponding normalization name; acquiring a concept code matched with the normalization name according to the mapping relation between the normalization name and the concept code; and obtaining the ICD code according to the mapping relation between the concept code and the ICD code.
Preferably, the method for obtaining the corresponding normalized name includes:
judging whether the disease query statement has a query history;
if yes, obtaining an ICD code according to the query history;
if not, matching the disease query statement with the ontology model;
judging whether the matching is accurate;
if the matching is accurate, obtaining a normalized name;
and if the matching is not accurate, matching the disease query statement with the ontology model according to the text similarity to obtain a similar normalized name.
The preferred ICD coding query method of the present invention further includes a method of matching through parent node terms:
matching the disease query statement with the father node term of the disease name to obtain the father node concept code;
and obtaining the ICD code according to the father node concept code.
Preferably, the ICD coding query method of the present invention further includes a coding output method:
checking the ICD codes, and sequencing according to the medical term set or the weight to obtain a code list;
and outputting the coding list.
The invention also provides a query system for realizing the ICD code query method, which is characterized by comprising a query module, a matching module and a mapping module;
the query module is used for acquiring a disease query statement;
the matching module is used for matching the disease query statement with the normalization name or the disease name in the ontology model to obtain a corresponding normalization name;
the mapping module is used for obtaining the concept code matched with the normalization name according to the mapping relation between the normalization name and the concept code; and obtaining the ICD code according to the mapping relation between the concept code and the ICD code.
Compared with the prior art, the invention has the beneficial effects that: semantically normalizing the disease names through the ontology model to solve the problem of text difference of disease name expression and improve the accuracy of ICD coding; through the mapping of the normalized name and the concept code and the mapping of the concept code and the ICD code, even the ICD codes of different versions or regions can obtain effective mapping so as to adapt to the ICD codes of different versions and improve the applicability and the reusability.
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FIG. 1 is a flow chart of the ICD encoding method of the present invention;
FIG. 2 is a logical block diagram of the encoding system and query system of the present invention;
FIG. 3 is a flow chart of an ICD encoding query method of the present invention;
fig. 4 is a flow chart of a method of an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention is described in further detail below with reference to the attached drawing figures:
an ICD coding method, as shown in fig. 1, includes:
step 101: a set of medical terms is obtained, the set of medical terms including a disease name. The set of medical terms may include terms in any one of the following fields or combinations thereof: diseases, symptoms, anatomy, surgery, biology, medicine, medical devices, examinations, imaging, nursing, genetic, spoken language, and the like.
Step 102: and establishing an ontology model according to the disease name and the normalization name thereof. The normalization names are used for normalizing the semantic concepts of the disease names, such as fever, body temperature rise, 38 degrees body temperature, and the like, and after the semantic concepts are the same, they are normalized to have the same normalization names.
Step 103: and establishing a mapping relation between the normalized name and the concept code.
Step 104: and establishing a mapping relation between the concept codes and the ICD codes. The ICD code comprises any one of the following coding systems or the combination thereof: ICD-10, ICD-9, ICD-11, ICD-9-CM-3, and ICD-O, but are not so limited. Namely, the ICD codes of different coding systems are normalized by conceptual coding.
Semantically normalizing the disease names through the ontology model to solve the problem of text difference of disease name expression and improve the accuracy of ICD coding; through the mapping of the normalized name and the concept code and the mapping of the concept code and the ICD code, even the ICD codes of different versions or regions can obtain effective mapping so as to adapt to the ICD codes of different versions and improve the applicability and the reusability.
Ontology (Ontology) is a conceptual model describing concepts and relationships between concepts, and describes definitions of concepts through relationships between concepts; mainly oriented to specific fields and used for describing a conceptual model of the specific fields. There are 5 basic modeling primitive: classes, relationships, functions, axioms, and instances. Classes (classes) include names of concepts, collections of relationships with other concepts, and descriptions of concepts in natural language; relationships interactions between concepts in the domain, such as child node relationships, semantically corresponding to a set of object tuples; functions (functions) are a special type of relationship, such as a parent node relationship or a parent node relationship; axioms (axioms) represent eternal assertions, such as concept B falls within the scope of concept a; instances (instances) represent elements, semantically representing objects, representing the actual existence of a particular class.
In one embodiment, the ontology model is a data model of an association group class, the model comprises entities and relations, one entity is a node, and one relation is a connecting line between two related nodes, and the construction of the ontology model realizes high integration of ICD coding semantic relations.
In step 102, the method for establishing the ontology model includes:
step 201: the area and category of expertise are determined.
Step 202: the possibility of reusing existing ontologies is examined.
Step 203: a list of medical terms is listed.
Step 204: defining classes and classes' hierarchies.
Step 205: defining attributes of classes, wherein the attributes of the classes comprise intrinsic attributes, extrinsic attributes and relations with other classes, the intrinsic attributes are used for describing occurrence parts, morphological changes and the like of diseases, and the extrinsic attributes are used for describing treatment methods and the like.
Step 206: defining a facet of the attribute, wherein the facet comprises a value type, a domain and a value range; and defining axioms.
Step 207: an instance of the class is created.
For example, the class of disease names defines the hierarchy of classes, such as disease, symptom, etc. as a primary class, and further such as psychiatric and medical, etc. as a secondary class; taking the normalized name as an axiom; using the relationships to define child node relationships; the function is used for defining the relation of the father node; terms in the medical terminology or disease names are taken as examples. But is not limited thereto.
The method for optimizing the ontology model comprises the following steps:
step 211: an ontology model is determined that segments the domain of expertise.
Step 212: and fusing the plurality of ontology models according to an ontology integration method to obtain a fused ontology model. The ontology models in different medical fields can be fused according to the relationship between the ontology models.
Step 213: and continuously optimizing and correcting the fusion ontology model.
The invention also provides an encoding system for implementing the ICD encoding method, as shown in fig. 2, including a medical term set 11, an ontology model building module 2 and a mapping building module 3;
the set of medical terms 11 includes the name of the disease;
the ontology model building module 2 is used for building an ontology model 13 according to the disease name and the normalized name thereof;
the mapping construction module 3 is used for establishing the mapping relation between the normalized name and the concept code according to the medical term set 11 and the ICD code library 12; and establishing a mapping relation between the concept codes and the ICD codes to obtain a mapping library 14.
The ICD coding library 12 is used for recording ICD versions covering national provinces and direct prefectures, and the ontology model is used for carrying out concept normalization on all recorded ICD versions at a semantic level.
The invention also provides an ICD code query method, as shown in fig. 3, the ICD code query method includes:
step 301: and acquiring a disease query statement. The disease query statement may be a set of keywords or a piece of text. The disease query statement can be preprocessed to meet the standard format requirement; the text may also be word segmented to extract keywords.
Step 302: and acquiring an ontology model, wherein the ontology model is used for acquiring the normalized name of the disease according to the disease name. The disease names include terms in any one of the following fields or combinations thereof: diseases, symptoms, anatomy, surgery, biology, medicine, medical devices, examinations, imaging, nursing, genetic, spoken language, etc.
Step 303: and matching the disease query statement with the normalization name or the disease name in the ontology model to obtain the corresponding normalization name. And obtaining the normalized name in a semantic parsing mode so as to cope with diversified description modes.
Step 304: and obtaining the concept code matched with the normalization name according to the mapping relation between the normalization name and the concept code.
Step 305: and obtaining the ICD code according to the mapping relation between the concept code and the ICD code.
By mapping the concept codes and the ICD codes, the ICD codes of different versions or regions can be effectively mapped to adapt to the ICD codes of different versions, and the applicability and the reusability are improved.
Examples
As shown in fig. 4, the ICD code query method includes:
step 401: and inputting a disease query statement.
Step 402: and preprocessing a disease query statement. Such as data cleaning, keyword extraction, etc.
Step 403: it is determined whether the disease query statement has a query history.
If yes, go to step 404, obtain ICD codes according to the query history, go to step 411.
If not, go to step 405: the disease query statement is matched against the ontology model, and step 406 is performed. Wherein, matching refers to matching with the disease name or normalized name in the ontology model.
Step 406: and judging whether the matching is accurate matching.
If the matching is accurate, go to step 407: the normalized name is obtained and step 409 is performed.
If there is no exact match, go to step 408: and matching the disease query statement with the ontology model according to the text similarity to obtain a similar normalized name. I.e., semantic synonym matching.
Step 409: and mapping the normalized name with the concept code and the ICD code respectively to obtain the ICD code.
Step 410: and checking the ICD codes, and sequencing according to the medical term set or the weight to obtain a code list. The verification can be performed through a rule algorithm according to a verification rule base; the recommended order may also be arranged in the form of an array.
Step 411: and outputting the coding list or the ICD codes. The output includes a single precision encoded output and a plurality of recommended encoded outputs.
The ICD code query method can provide services in an API (application programming interface) interface mode, and can be efficiently and quickly docked with a third-party system or platform for debugging and use.
In step 408, when no normalized name with higher similarity is obtained, the matching method may also be performed through the parent node term:
matching the disease query statement with the father node term of the disease name to obtain the father node concept code, wherein the father node term can be obtained from the functional relationship in the ontology model;
and obtaining the ICD code according to the father node concept code. The parent term refers to a disease name that has a parent relationship with the current query disease name.
Through test verification, the accuracy of ICD-10 coding query reaches more than 97%, wherein the accurate coding proportion is 92%, and the recommended coding proportion is 8%. Meanwhile, as the quality of an input word is higher, the accuracy of giving a code is higher. The ICD coding query method solves the problems of diversified expression coding of clinical diagnosis and ICD-10 regional version difference, greatly improves the accuracy of clinical ICD-10 coding such as disease diagnosis names and the like, and enhances the accuracy and consistency of medical diagnosis information coding results in an ICD coding range; the method has strong reusability and is widely suitable for ICD coding systems with version difference problems.
The invention also provides an inquiry system for implementing the ICD code inquiry method, as shown in FIG. 2,
comprises a query module 21, a matching module 22, a mapping module 23, a checking module 24 and an output module 25;
the query module 21 is configured to obtain a disease query statement;
the matching module 22 is configured to match the disease query statement with the normalization name or the disease name in the ontology model, and obtain a corresponding normalization name;
the mapping module 23 is configured to obtain a concept code matched with the normalization name according to a mapping relationship between the normalization name and the concept code in the mapping library 14; obtaining an ICD code according to the mapping relation between the concept code and the ICD code in the mapping library 14;
the checking module 24 is used for checking the ICD code; the output module 25 is used for outputting ICD codes.
The inquiry system automatically encodes the medical information input by the user, and solves the problem of cross-version standardization of ICD codes.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An ICD encoding method, comprising:
obtaining a set of medical terms, the set of medical terms including a disease name;
establishing an ontology model according to the disease name and the normalization name thereof;
establishing a mapping relation between the normalized name and the concept code;
and establishing a mapping relation between the concept codes and the ICD codes.
2. The ICD encoding method of claim 1, wherein the set of medical terms further includes terms in any one of the following fields or combinations thereof:
symptoms, anatomy, surgery, biology, medicine, medical instruments, exam, imaging, care, genetic, and spoken language;
the ICD code comprises any one of the following coding systems or the combination thereof:
ICD-10, ICD-9, ICD-11, ICD-9-CM-3, and ICD-O.
3. The ICD coding method of claim 1, wherein the method of building an ontology model comprises:
determining professional fields and categories;
examining the possibility of reusing the existing ontology;
listing a medical term list;
defining classes and class hierarchies;
defining attributes of a class, wherein the attributes of the class comprise intrinsic attributes, extrinsic attributes and relations with other classes;
defining a facet of the attribute, wherein the facet comprises a value type, a domain and a value range;
an instance is created.
4. The ICD encoding method of claim 3, further comprising a method of ontology model optimization:
determining an ontology model for subdividing the professional field;
fusing the plurality of ontology models according to an ontology integration method to obtain a fused ontology model;
and continuously optimizing and correcting the fusion ontology model.
5. An encoding system for implementing the ICD encoding method as claimed in any one of claims 1-4, comprising a medical term set, an ontology model building module and a mapping building module;
the set of medical terms includes a disease name;
the ontology model building module is used for building an ontology model according to the disease name and the normalized name thereof;
the mapping construction module is used for establishing the mapping relation between the normalization name and the concept code according to the medical term set and the ICD code library; establishing a mapping relation between the concept codes and the ICD codes;
the ICD code library comprises one or more versions of ICD codes.
6. An ICD code query method is characterized by comprising the following steps:
acquiring a disease query statement;
acquiring an ontology model, wherein the ontology model is used for acquiring a normalized name of a disease according to the disease name;
matching the disease query statement with the normalization name or the disease name in the ontology model to obtain a corresponding normalization name;
acquiring a concept code matched with the normalization name according to the mapping relation between the normalization name and the concept code;
and obtaining the ICD code according to the mapping relation between the concept code and the ICD code.
7. The ICD coding query method of claim 6, wherein the method of obtaining the corresponding normalized name comprises:
judging whether the disease query sentence has a query history;
if yes, obtaining an ICD code according to the query history;
if not, matching the disease query statement with the ontology model;
judging whether the matching is accurate;
if the matching is accurate, obtaining a normalized name;
and if the matching is not accurate, matching the disease query statement with the ontology model according to the text similarity to obtain a similar normalized name.
8. The ICD coding query method of claim 6, further comprising a method of matching by parent node terms:
matching the disease query statement with the father node term of the disease name to obtain the father node concept code;
and obtaining the ICD code according to the father node concept code.
9. The ICD coding query method of claim 8, further comprising a coding output method:
checking the ICD codes, and sequencing according to the medical term set or the weight to obtain a code list;
and outputting the coding list.
10. A query system for implementing the ICD code query method according to any one of claims 6-9, comprising a query module, a matching module and a mapping module;
the query module is used for acquiring a disease query statement;
the matching module is used for matching the disease query statement with the normalization name or the disease name in the ontology model to obtain a corresponding normalization name;
the mapping module is used for obtaining the concept code matched with the normalization name according to the mapping relation between the normalization name and the concept code; and obtaining the ICD code according to the mapping relation between the concept code and the ICD code.
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