Named entity category labeling method and system for Chinese electronic medical record

Abstract

The invention discloses a named entity category labeling method and system for a Chinese electronic medical record, and mainly solves the problem of how to more accurately identify and label named entities in the Chinese electronic medical record under the multi-classification problem based on a joint neural network model. The method mainly comprises the steps of obtaining word vector representations of Chinese words, pre-extracting structured features of entities based on a HmapCNN model, extracting context sequence features of the entities based on a bidirectional LSTM model, performing sequence labeling of the entities based on CRF, and obtaining a final labeling result. The model is further packaged, an interaction system based on a B / S framework is designed and developed, a graphical display interface friendly to a user is provided, and processing such as use of the model and display and export of a result is facilitated.

Description

technical field [0001] The invention belongs to natural language processing, and relates to a method and system for marking named entity categories of Chinese electronic medical records. Specifically, it refers to the feature extraction and category labeling of entities based on HmapCNN-BiLSTM-CRF. Background technique [0002] Electronic medical records can electronically store the patient's lifelong health status and disease and medical history, which is an inevitable trend in the development of the medical field in the information age. The text mining of Chinese electronic medical records can effectively use information to provide accurate data access, warning and auxiliary decision-making functions for clinical diagnosis and treatment. The labeling of named entity categories based on Chinese electronic medical records is an important part of it, which is of great significance to the extraction of relationships between entities and medical knowledge reasoning. [0003] ...

AI Technical Summary

Problems solved by technology

[0008] Aiming at the problem of incomplete classification of Chinese electronic medical record named entity categories and limited accuracy of neural network model entity labeling under the problem of multi-category labeling, the present invention provides a Chinese electronic medical record named entity category labeling method based on the HmapCNN-BiLSTM-CRF joint model And the system, to a certain extent, solves the problem of limited feature extraction and recognition accuracy of a single model for multi-category labeling problems

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