- Title Information
- Title
- Automated extraction of substance use information from clinical texts
- Name:
Personal
- Name Part
- Wang, Yan
- Role
- Role Term:
Text (marcrelator)
- author
- affiliation
- University of Minnesota. Institute for Health Informatics
- Name:
Personal
- Name Part
- Chen, Elizabeth S.
- Role
- Role Term:
Text (marcrelator)
- author
- affiliation
- Brown University. Center for Biomedical Informatics
- Name:
Personal
- Name Part
- Arsoniadis, Elliot
- Role
- Role Term:
Text (marcrelator)
- author
- affiliation
- University of Minnesota. Institute for Health Informatics
- Name:
Personal
- Name Part
- Carter, Elizabeth W.
- Role
- Role Term:
Text (marcrelator)
- author
- affiliation
- University of Vermont. Center for Clinical and Translational Science
- Name:
Personal
- Name Part
- Lindemann, Elizabeth
- Role
- Role Term:
Text (marcrelator)
- author
- affiliation
- University of Minnesota. Institute for Health Informatics
- Name:
Personal
- Name Part
- Sarkar, Indra Neil
- Role
- Role Term:
Text (marcrelator)
- author
- affiliation
- Brown University. Center for Biomedical Informatics
- Name:
Personal
- Name Part
- Melton, Genevieve B.
- Role
- Role Term:
Text (marcrelator)
- author
- affiliation
- University of Minnesota. Institute for Health Informatics
- Type of Resource
- text
- Genre (aat)
- papers (documents)
- Origin Information
- Date Created
(keyDate="yes", encoding="w3cdtf")
- 2015
- Physical Description
- Extent
- p. 2121-2130
- Language
- Language Term:
Code (ISO639-2B)
- eng
- 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.
- Note
- This work is funded by National Library of Medicine/National Institutes of Health grant R01LM011364.
- Subject (LCSH)
- Topic
- Drug use
- Subject (LCSH)
- Topic
- Alcohol use
- Subject (LCSH)
- Topic
- Tobacco use
- Related Item:
Host
(displayLabel="Published in")
- Title Information
- Title
- AMIA Annual Symposium Proceedings
- Part Number
- 2015:2121-30
- Access Condition:
use and reproduction
(href="https://creativecommons.org/licenses/by/4.0")
- This work is licensed under a Creative Commons CC BY 4.0 License
- Access Condition:
logo
(href="https://licensebuttons.net/l/by/4.0/88x31.png")
- Identifier:
PMID
(displayLabel="PubMed ID")
- 26958312