Depressive Symptoms and Functional Impairments Extraction From Electronic Health Records

Abstract

This study aims to extract symptom profiles and functional impairments of major depressive disorder from electronic health records (EHRs). A chart review was conducted by three annotators on 500 discharge notes randomly selected from a medical center in Taiwan to compile annotated corpora for nine depressive symptoms and four types of functional impairment. Named entity recognition techniques including the dictionary-based approach., a conditional random field model, and deep learning approaches were developed for the task of recognizing depressive symptoms and functional impairments from EHRs. The results show that the average micro-F-measures of the supervised learning approaches in extracting depressive symptoms is almost perfect (>0.90) but less accurate for the extraction of functional impairment.