Suicide theory-guided natural language processing of clinical progress notes to improve prediction of veteran suicide risk: protocol for a mixed-method study.

Autor:	Meerwijk EL; VA Health Services Research & Development, Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California, USA esther.meerwijk@va.gov., Tamang SR; VA Health Services Research & Development, Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California, USA.; Department of Biomedical Data Science, Stanford University, Stanford, California, USA., Finlay AK; VA Health Services Research & Development, Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California, USA.; Schar School of Policy and Government, George Mason University, Arlington, Virginia, USA.; VA National Center on Homelessness Among Veterans, Durham, North Carolina, USA., Ilgen MA; Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA.; VA Health Services Research & Development, Center for Clinical Management Research, VA Ann Arbor Health Care System, Ann Arbor, Michigan, USA., Reeves RM; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.; VA Health Sevices Research & Development, VA Tennessee Valley Health Care System, Nashville, Tennessee, USA., Harris AHS; VA Health Services Research & Development, Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California, USA.; Stanford-Surgical Policy Improvement Research and Education Center, Stanford University School of Medicine, Stanford, California, USA.
Jazyk:	angličtina
Zdroj:	BMJ open [BMJ Open] 2022 Aug 24; Vol. 12 (8), pp. e065088. Date of Electronic Publication: 2022 Aug 24.
DOI:	10.1136/bmjopen-2022-065088
Abstrakt:	Introduction: The state-of-the-art 3-step Theory of Suicide (3ST) describes why people consider suicide and who will act on their suicidal thoughts and attempt suicide. The central concepts of 3ST-psychological pain, hopelessness, connectedness, and capacity for suicide-are among the most important drivers of suicidal behaviour but they are missing from clinical suicide risk prediction models in use at the US Veterans Health Administration (VHA). These four concepts are not systematically recorded in structured fields of VHA's electronic healthcare records. Therefore, this study will develop a domain-specific ontology that will enable automated extraction of these concepts from clinical progress notes using natural language processing (NLP), and test whether NLP-based predictors for these concepts improve accuracy of existing VHA suicide risk prediction models. Methods and Analysis: Our mixed-method study has an exploratory sequential design where a qualitative component (aim 1) will inform quantitative analyses (aims 2 and 3). For aim 1, subject matter experts will manually annotate progress notes of clinical encounters with veterans who attempted or died by suicide to develop a domain-specific ontology for the 3ST concepts. During aim 2, we will use NLP to machine-annotate clinical progress notes and derive longitudinal representations for each patient with respect to the presence and intensity of hopelessness, psychological pain, connectedness and capacity for suicide in temporal proximity of suicide attempts and deaths by suicide. These longitudinal representations will be evaluated during aim 3 for their ability to improve existing VHA prediction models of suicide and suicide attempts, STORM (Stratification Tool for Opioid Risk Mitigation) and REACHVET (Recovery Engagement and Coordination for Health - Veterans Enhanced Treatment). Ethics and Dissemination: Ethics approval for this study was granted by the Stanford University Institutional Review Board and the Research and Development Committee of the VA Palo Alto Health Care System. Results of the study will be disseminated through several outlets, including peer-reviewed publications and presentations at national conferences. Competing Interests: Competing interests: None declared. (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
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