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Using General-purpose Sentiment Lexicons for Suicide Risk Assessment in Electronic Health Records: Corpus-Based Analysis

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Original languageEnglish
Article numbere22397
Pages (from-to)e22397
JournalJMIR Medical Informatics
Volume9
Issue number4
DOIs
Published13 Apr 2021

Bibliographical note

Funding Information: The authors wish to thank Jefrey Lijffijt and Paul Rayson for their advice on corpus linguistics and James Pennebaker for permission to use the LIWC lexicon. Any errors are the authors’ own. RD is funded by a Clinician Scientist Fellowship (project e-HOST-IT) from the Health Foundation in partnership with the Academy of Medical Sciences, which also funds AB. This work was also partly supported by Health Data Research UK, an initiative funded by UK Research and Innovation, Department of Health and Social Care (England), and the devolved administrations, leading medical research charities, and the Maudsley Charity. This paper represents independent research partly funded (AR, RD, SV, and AB) by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funding Information: The authors wish to thank Jefrey Lijffijt and Paul Rayson for their advice on corpus linguistics and James Pennebaker for permission to use the LIWC lexicon. Any errors are the authors' own. RD is funded by a Clinician Scientist Fellowship (project e-HOST-IT) from the Health Foundation in partnership with the Academy of Medical Sciences, which also funds AB. This work was also partly supported by Health Data Research UK, an initiative funded by UK Research and Innovation, Department of Health and Social Care (England), and the devolved administrations, leading medical research charities, and the Maudsley Charity. This paper represents independent research partly funded (AR, RD, SV, and AB) by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2021 André Bittar, Sumithra Velupillai, Angus Roberts, Rina Dutta. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors

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.

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