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ICD-11 code retrieval method based on natural semantic processing and knowledge graph

A technology of ICD-11 and knowledge graph, applied in natural language data processing, semantic analysis, electronic digital data processing, etc., can solve problems such as incompatibility, wrong combination, cumbersome operation, etc., and reduce manpower consumption cost and communication cost , Ensure a high degree of consistency and effective management decisions

Active Publication Date: 2021-05-25
SHAN DONG MSUN HEALTH TECH GRP CO LTD
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0006] But the inventor finds that the retrieval of ICD codes can only be retrieved by keywords or pinyin brevity codes at present. For ICD-11, only the codes of each part in the diagnosis can be retrieved, and then combined together, which is inconvenient to use and Inaccurate; on the one hand, due to the incompatibility between clinical terms and ICD-11 standard diagnostic coding terms, on the other hand, the retrieval of each part is very cumbersome for clinicians and coders, and a diagnosis needs to be searched many times to obtain Combines an encoded result and is prone to wrong combinations

Method used

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  • ICD-11 code retrieval method based on natural semantic processing and knowledge graph
  • ICD-11 code retrieval method based on natural semantic processing and knowledge graph
  • ICD-11 code retrieval method based on natural semantic processing and knowledge graph

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Embodiment 1

[0049] Such as Figure 1-15 As shown, Embodiment 1 of the present disclosure provides an ICD-11 code retrieval method based on natural semantic processing and knowledge graph, including the following steps:

[0050] S1: Natural language processing of free-hand written clinical diagnoses.

[0051] Perform entity recognition and entity relationship recognition on the input original clinical diagnosis, and mark entities and their entity types, such as disease abnormalities, anatomical parts, organ tissues, properties, typing, stages, etiology, clinical manifestations, microorganisms, chemical substances etc., and then mark out the modification and restriction relationships between entities.

example

[0052] Example: acute left Escherichia coli pyelonephritis.

[0053] This embodiment marks the relationship between entities and entities for this diagnosis, such as figure 1 shown.

[0054] Entities are marked for this diagnosis: ①renal pelvis, entity type is anatomical site; ②Escherichia coli, entity type is microorganism; ③nephritis, entity type is disease or abnormal; ④left side, entity type is orientation; ⑤acute, entity Type is property.

[0055] There are four groups of entity relations, which are: "renal pelvis" as an anatomical site modifier restricts disease abnormality: nephritis; "left side" as an orientation modifier restricts anatomical site: renal pelvis; "acute" as a period modifier restricts disease abnormality: nephritis; Bacteria as Microbial (Etiological) Modifications Limiting Disease Abnormalities: Nephritis.

[0056] Entity and type recognition uses the entity concept description dictionary in the self-maintained medical knowledge map, as well as term...

Embodiment 2

[0177] Embodiment 2 of the present disclosure provides an ICD-11 code retrieval system based on natural semantic processing and knowledge graph, including:

[0178] The data acquisition module is configured to: acquire freely written clinical diagnosis text data;

[0179] The entity recognition module is configured to: perform natural language processing on the acquired text data, obtain entity and entity relationship recognition results, and mark entities and their entity types;

[0180] The knowledge map labeling module is configured to: mark other entities directly connected to the entity on the medical knowledge map, and record the relationship weight coefficient;

[0181] The candidate code search module is configured to: combine the entity relationship and the relationship weight coefficient on the medical knowledge map, and search for the candidate code through the tree structure of the standard diagnosis entity and entity relationship;

[0182] The coding combination ...

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Abstract

The invention provides an ICD-11 code retrieval method based on natural semantic processing and a knowledge graph. The method comprises the following steps: acquiring freely written clinical diagnosis text data; processing natural language of the obtained text data to obtain an entity and entity relationship recognition result, and marking the entity and the entity type to which the entity belongs; marking other entities directly associated with the entity on the medical knowledge graph, and recording a relationship weight coefficient; in combination with the entity relationship and the relationship weight coefficient on the medical knowledge graph, searching candidate codes by the tree structure of the entity and the entity relationship of the standard diagnosis; searching and screening the combination formed by the candidate codes according to an ICD-11 coding rule, and selecting the most reasonable combination code as an ICD-11 diagnosis code; the intelligence of ICD-11 diagnosis code retrieval is realized, the problem that a medical institution has no code or is insufficient in code allocation is solved, and the manpower consumption cost and the communication cost are reduced.

Description

technical field [0001] The present disclosure relates to the technical field of data processing, in particular to an ICD-11 code retrieval method based on natural semantic processing and knowledge graph. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] ICD (International Classification of Diseases), as an international classification standard for diseases and related health problems, is an important part of the health information standard system. It has a history of more than 100 years since its inception, during which it has undergone several revisions, from the initial statistics only for the causes of death to the statistical classification involving all diseases and causes of death, including injuries and poisoning and their external causes. Disease classification is to classify diseases according to certain characteristics according...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/295G06F40/30G16H50/70
CPCG06F40/295G06F40/30G16H50/70
Inventor 桑波孙钊高希余樊昭磊李森李福友
Owner SHAN DONG MSUN HEALTH TECH GRP CO LTD
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