Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

TL;DR

This paper presents a CNN-based approach for automatic diagnosis from electronic medical records, achieving high accuracy and recall without relying on manually constructed knowledge bases or rule matching.

Contribution

It introduces a novel CNN-based method that automatically extracts semantic features from EMRs for diagnosis, eliminating the need for artificial rules or knowledge bases.

Findings

Achieved 98.67% accuracy in diagnosis

Achieved 96.02% recall in diagnosis

Demonstrated feasibility and effectiveness of CNN in clinical diagnosis

Abstract

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.