EMR information association and evolution method based on graph deep learning

Abstract

The invention discloses an EMR information association and evolution method based on graph deep learning, and the method comprises the following steps: preprocessing EMR data: obtaining an EMR data set, and obtaining an entity dictionary of EMR; constructing an EMR graph: converting words in the entity dictionary into vector representation, obtaining a vector matrix of the EMR graph and an adjacent matrix of the EMR graph, and performing combining to form the EMR graph; constructing an EMR image deep learning model: constructing an input image data set of the EMR image deep learning model according to all EMR images corresponding to the obtained EMR data set so as to further obtain an image deep learning model F; and carrying out EMR information association and evolution: feeding any pieceof EMR data into the graph deep learning model F, and constructing an evolution sequence of the EMR graph. The invention provides an EMR information association and evolution method based on graph deep learning, and the method comprises the steps: carrying out the modeling of electronic medical record data through a graph deep learning method, displaying the evolution process of the structural information of an electronic medical record through a network relation graph visualization technology, and achieving the knowledge discovery and interpretable deep learning.

Description

technical field [0001] The invention relates to the field of natural language processing and deep learning, in particular to an EMR information association and evolution method based on graph deep learning. Background technique [0002] Clinical texts contain a wealth of health and medical information, and clinical texts represented by Electronic Medical Records (EMR) are an important information resource generated during medical activities. Electronic medical records are divided into outpatient electronic medical records and inpatient electronic medical records, including sociodemographic information, chief complaints, history of present illness, examination records, disease diagnosis, etc. It can be seen that electronic medical records are multi-source heterogeneous data collections with highly dense medical knowledge, including rich entities, such as symptoms, diseases, examinations, etc., and some medical relationships are often hidden between these entities. Currently,...

AI Technical Summary

Problems solved by technology

However, most electronic medical records are stored in the form of unstructured text, which leads to low application efficiency of medical records, hinders the degree of medical informatization, and clinical workers cannot clearly obtain structured related information and medical knowledge of patients' conditions.

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