ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
Using Neural Multi-task Learning to Extract Substance Abuse Information from Clinical Notes.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Decoding substance use disorder severity from clinical notes using a large language model.Npj mental health research · 2025Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- MIMIC-SBDH: A Dataset for Social and Behavioral Determinants of Health.Proceedings of machine learning research · 2021Article
- Annotating social determinants of health using active learning, and characterizing determinants using neural event extraction.Journal of biomedical informatics · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
30815184PMC6371261W2941102086What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.