Description
- Abstract:
- Within clinical discourse, social history (SH) includes important information about substance use (alcohol, drug, and nicotine use) as key risk factors for disease, disability, and mortality. In this study, we developed and evaluated a natural language processing (NLP) system for automated detection of substance use statements and extraction of substance use attributes (e.g. temporal and status) based on Stanford Typed Dependencies. The developed NLP system leveraged linguistic resources and domain knowledge from a multi-site social history study, Propbank and the MiPACQ corpus. The system attained F-scores of 89.8, 84.6 and 89.4 respectively for alcohol, drug, and nicotine use statement detection, as well as average F-scores of 82.1, 90.3, 80.8, 88.7 96.6, and 74.5 respectively for extraction of attributes. Our results suggest that NLP systems can achieve good performance when augmented with linguistic resources and somain knowledge when applied to a wide breadth of substance use free text clinical notes.
- Notes:
- This work is funded by National Library of Medicine/National Institutes of Health grant R01LM011364.
Access Conditions
- Use and Reproduction
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- This work is licensed under a Creative Commons CC BY 4.0 License
Citation
Wang, Yan, Chen, Elizabeth S., Arsoniadis, Elliot, et al.,
"Automated extraction of substance use information from clinical texts"
(2015).
SFHERE Publications and Presentations.
Brown Digital Repository. Brown University Library.
https://repository.library.brown.edu/studio/item/bdr:659402/
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Automated extraction of substance use information from clinical texts
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SFHERE Publications and Presentations
This collection houses journal articles, conference papers and presentations, and abstracts and posters authored by members of the SFHERE team....