Name
Title | ||
|---|---|---|
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 (u003e0.90) but less accurate for the extraction of functional impairment. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICMLC48188.2019.8949199 | ICMLC |
Field | DocType | Citations |
Conditional random field,Computer science,Supervised learning,Functional impairment,Artificial intelligence,Chart,Deep learning,Major depressive disorder,Artificial neural network,Named-entity recognition,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| You-Chen Zhang | 1 | 0 | 0.68 |
| Chung-Hong Lee | 2 | 0 | 0.34 |
| Tyng-Yeu Liang | 3 | 0 | 0.34 |
| Wei-Che Chung | 4 | 0 | 0.34 |
| Kuei-Han Li | 5 | 0 | 0.34 |
| Cheng-Chieh Huang | 6 | 0 | 0.34 |
| Hong-Jie Dai | 7 | 1 | 2.04 |
| Chi-Shin Wu | 8 | 0 | 1.01 |
| Chian-Jue Kuo | 9 | 0 | 0.34 |
| Chu-Hsien Su | 10 | 0 | 1.69 |
| Horng-Chang Yang | 11 | 0 | 0.34 |