Depressive Symptoms and Functional Impairments Extraction From Electronic Health Records
| Autor: | Horng-Chang Yang, Chu-Hsien Su, Cheng-Chieh Huang, Wei-Che Chung, Kuei-Han Li, Hong-Jie Dai, Chung-Hong Lee, Tyng-Yeu Liang, Chi-Shin Wu, Chian Jue Kuo, You-Chen Zhang |
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| Rok vydání: | 2019 |
| Předmět: |
Conditional random field
business.industry Deep learning Supervised learning 02 engineering and technology Health records medicine.disease computer.software_genre Named-entity recognition 020204 information systems Chart review 0202 electrical engineering electronic engineering information engineering Medicine Major depressive disorder 020201 artificial intelligence & image processing Artificial intelligence business computer Depressive symptoms Clinical psychology |
| Zdroj: | ICMLC |
| DOI: | 10.1109/icmlc48188.2019.8949199 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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