Enhancing Suicide Attempt Risk Prediction Models with Temporal Clinical Note Features
This study demonstrated that incorporating electronic health record-derived clinical note features enhanced suicide attempt risk prediction models, particularly with window-temporalized LSTM models. Our results underscored the critical value of unstructured data in suicidality prediction, aligning with previous findings. Future research should focus on integrating more sophisticated methods to continue improving prediction accuracy, which will enhance the effectiveness of future intervention.
Authors
- Kevin R. Krause (ORCID: https://orcid.org/0000-0001-8652-753X)
- Sharon E. Davis (ORCID: https://orcid.org/0000-0003-0792-8867)
- Colin G. Walsh (ORCID: https://orcid.org/0000-0002-9379-2056)
- S. Trent Rosenbloom (ORCID: https://orcid.org/0000-0001-7455-2260)
- Katherine Musacchio Schafer (ORCID: https://orcid.org/0000-0002-3744-1518)
- Zhijun Yin (ORCID: https://orcid.org/0000-0002-3075-1337)
Institutions
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Applied Clinical Informatics
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1055/a-2411-5796
- Citations
- 3
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- Vanderbilt University
- Vanderbilt University Medical Center
- U.S. National Library of Medicine
- National Center for Research Resources