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Using General-purpose Sentiment Lexicons for Suicide Risk Assessment in Electronic Health Records: Corpus-Based Analysis
- Source :
- JMIR Medical Informatics, Vol 9, Iss 4, p e22397 (2021), JMIR Medical Informatics
- Publication Year :
- 2021
- Publisher :
- JMIR Publications, 2021.
-
Abstract
- Background Suicide is a serious public health issue, accounting for 1.4% of all deaths worldwide. Current risk assessment tools are reported as performing little better than chance in predicting suicide. New methods for studying dynamic features in electronic health records (EHRs) are being increasingly explored. One avenue of research involves using sentiment analysis to examine clinicians’ subjective judgments when reporting on patients. Several recent studies have used general-purpose sentiment analysis tools to automatically identify negative and positive words within EHRs to test correlations between sentiment extracted from the texts and specific medical outcomes (eg, risk of suicide or in-hospital mortality). However, little attention has been paid to analyzing the specific words identified by general-purpose sentiment lexicons when applied to EHR corpora. Objective This study aims to quantitatively and qualitatively evaluate the coverage of six general-purpose sentiment lexicons against a corpus of EHR texts to ascertain the extent to which such lexical resources are fit for use in suicide risk assessment. Methods The data for this study were a corpus of 198,451 EHR texts made up of two subcorpora drawn from a 1:4 case-control study comparing clinical notes written over the period leading up to a suicide attempt (cases, n=2913) with those not preceding such an attempt (controls, n=14,727). We calculated word frequency distributions within each subcorpus to identify representative keywords for both the case and control subcorpora. We quantified the relative coverage of the 6 lexicons with respect to this list of representative keywords in terms of weighted precision, recall, and F score. Results The six lexicons achieved reasonable precision (0.53-0.68) but very low recall (0.04-0.36). Many of the most representative keywords in the suicide-related (case) subcorpus were not identified by any of the lexicons. The sentiment-bearing status of these keywords for this use case is thus doubtful. Conclusions Our findings indicate that these 6 sentiment lexicons are not optimal for use in suicide risk assessment. We propose a set of guidelines for the creation of more suitable lexical resources for distinguishing suicide-related from non–suicide-related EHR texts.
- Subjects :
- corpus linguistics
Poison control
Health Informatics
Risk management tools
02 engineering and technology
computer.software_genre
lcsh:Computer applications to medicine. Medical informatics
Suicide prevention
03 medical and health sciences
0302 clinical medicine
Health Information Management
Corpus linguistics
0202 electrical engineering, electronic engineering, information engineering
natural language processing
suicide
health care economics and organizations
Original Paper
Suicide attempt
business.industry
Sentiment analysis
risk assessment
psychiatry
Word lists by frequency
electronic health records
sentiment analysis
suicide, attempted
lcsh:R858-859.7
020201 artificial intelligence & image processing
Artificial intelligence
business
F1 score
Psychology
computer
030217 neurology & neurosurgery
Natural language processing
Subjects
Details
- Language :
- English
- ISSN :
- 22919694
- Volume :
- 9
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- JMIR Medical Informatics
- Accession number :
- edsair.doi.dedup.....6d484b19bc6caf61a1a1e4fe1b4d56f9