Name
Title | ||
|---|---|---|
Using Neural Multi-task Learning to Extract Substance Abuse Information from Clinical Notes. |
Abstract | ||
|---|---|---|
Substance abuse carries many negative health consequences. Detailed information about patients' substance abuse history is usually captured in free-text clinical notes. Automatic extraction of substance abuse information is vital to assess patients' risk for developing certain diseases and adverse outcomes. We introduce a novel neural architecture to automatically extract substance abuse information. The model, which uses multi-task learning, outperformed previous work and several baselines created using discrete models. The classifier obtained 0.88-0.95 F1 for detecting substance abuse status (current, none, past, unknown) on a withheld test set. Other substance abuse entities (amount, frequency, exposure history, quit history, and type) were also extracted with high-performance. Our results demonstrate the feasibility of extracting substance abuse information with little annotated data. Additionally, we used the neural multi-task model to automatically annotate 59.7K notes from a different source. Manual review of a subset of these notes resulted 0.84-0.89 precision for substance abuse status. |
Year | Venue | Field |
|---|---|---|
2018 | AMIA ... Annual Symposium proceedings. AMIA Symposium | Extract (substance),Multi-task learning,Computer science,Artificial intelligence,Natural language processing |
DocType | Volume | ISSN |
Conference | 2018 | 1942-597X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Lybarger | 1 | 4 | 1.42 |
| Meliha Yetisgen-Yildiz | 2 | 328 | 34.25 |
| Mari Ostendorf | 3 | 2462 | 348.75 |