Using Neural Multi-task Learning to Extract Substance Abuse Information from Clinical Notes

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.