icd-11 coding 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
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Embodiment 1
[0049] like 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 freely written clinical diagnoses.
[0051] Perform entity identification and entity relationship identification on the input original clinical diagnosis, and mark entities and their entity types, such as disease abnormalities, anatomical parts, organ tissues, properties, classification, staging, etiology, clinical manifestations, microorganisms, chemical substances And so on, and then mark out the decoration and restriction relationships between entities.
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[0052] Example: acute left-sided Escherichia coli pyelonephritis.
[0053] This embodiment marks the entity and the relationship between entities for the diagnosis, such as figure 1 shown.
[0054] Entity marked for this diagnosis: ① renal pelvis, entity type is anatomical site; ② Escherichia coli, entity type is microorganism; ③ nephritis, entity type is disease or abnormality; ④ left, entity type is orientation; ⑤ acute, entity type Type is nature.
[0055] There are four groups of entity relationships, which are: "renal pelvis" as an anatomical modifier to limit disease abnormalities: nephritis; "left" as an orientation modifier to limit anatomical sites: renal pelvis; "acute" as a period modifier to limit disease abnormalities: nephritis; "Bacteria" as microbial (causative) modifiers to limit disease abnormalities: nephritis.
[0056] The entity and type recognition uses the entity concept description dictionary in the self-maintained medical knowledge graph and the ter...
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 the entity and entity relationship recognition result, and mark the entity and the entity type to which it belongs;
[0180] The knowledge graph labeling module is configured to: label other entities directly related to the entity on the medical knowledge graph, 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 graph to search for the candidate code through the tree structure of the standard diagnosis entity and entity relationship;
[0182...
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