Depressive Symptoms and Functional Impairments Extraction from Electronic Health Records
Journal
Proceedings - International Conference on Machine Learning and Cybernetics
Journal Volume
2019-July
Pages
8949199
Date Issued
2019
Author(s)
Zhang Y.-C.
Lee C.-H.
Liang T.-Y.
Chung W.-C.
Li K.-H.
Huang C.-C.
Dai H.-J.
Kuo C.-J.
Su C.-H.
Yang H.-C.
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.
Type
conference paper