A multi-label classification method for electronic medical records based on symptom extraction and feature representation

Abstract

The invention provides a multi-label classification method of electronic medical record based on symptom extraction and feature representation. Considering the influence of disease and symptom and thecorrelation between symptoms on the multi-classification problem of disease label of electronic medical record, two different symptom representation methods are adopted: using TF-IDF builds symptom vectors and learns symptom vectors using word2vec. The two symptom vector sequences extracted from the electronic medical record are respectively used as input sequences of the two two-way LSTM models,and the two-way LSTM models are trained. For the electronic medical records of unknown disease tags, two symptom vectors corresponding to the symptoms are extracted to form two symptom vector sequences, and two trained bi-directional LSTM models are input respectively to obtain two probability vectors. The weighted combination of the two probability vectors is used to obtain the final classification vector. This method has good classification effect and applicability.

Description

technical field [0001] The invention belongs to the field of medical big data analysis, in particular to a multi-label classification method for electronic medical records based on symptom extraction and feature representation. Background technique [0002] The multi-label classification of electronic medical records (EMR) is an important task in the field of medical applications. Its purpose is to automatically generate disease labels for electronic medical records based on information such as symptoms, test indicators, drugs, and text in electronic medical records. , not only can save the cost of large-scale electronic medical record management and maintenance, but also provide convenience for medical knowledge mining and application. The multi-label classification based on electronic medical records can also be used in auxiliary diagnosis systems and hospital guidance systems, which can greatly improve doctors' work efficiency and shorten patients' visit time. The multi-...

AI Technical Summary

Problems solved by technology

The multi-label classification of electronic medical records relies on the features extracted from the medical record text. Currently, there are methods based on the entire text information, but there is a large amount of redundant information in the full text data that affects the classification effect; there are also inspection and detection indicators based on records in the text, Index information such as clinical data, medical codes, and drugs, but due to the lack of relevant information in some electronic medical records, these methods cannot be used

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