Department of Emergency Medicine; Department of Population and Quantitative Health Sciences
Artificial Intelligence and Robotics | Health Information Technology | Health Services Research | Psychiatry | Psychiatry and Psychology
Objective: Early identification of individuals who are at risk for suicide is crucial in supporting suicide prevention. Machine learning is emerging as a promising approach to support this objective. Machine learning is broadly defined as a set of mathematical models and computational algorithms designed to automatically learn complex patterns between predictors and outcomes from example data, without being explicitly programmed to do so. The model's performance continuously improves over time by learning from newly available data.
Method: This concept paper explores how machine learning approaches applied to healthcare data obtained from electronic health records, including billing and claims data, can advance our ability to accurately predict future suicidal behavior.
Results: We provide a general overview of machine learning concepts, summarize exemplar studies, describe continued challenges, and propose innovative research directions.
Conclusion: Machine learning has potential for improving estimation of suicide risk, yet important challenges and opportunities remain. Further research can focus on incorporating evolving methods for addressing data imbalances, understanding factors that affect generalizability across samples and healthcare systems, expanding the richness of the data, leveraging newer machine learning approaches, and developing automatic learning systems.
machine learning, mental health, neural network analysis, predictive analytics, suicide prevention
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Copyright © 2021 Boudreaux, Rundensteiner, Liu, Wang, Larkin, Agu, Ghosh, Semeter, Simon and Davis-Martin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
DOI of Published Version
Boudreaux ED, Rundensteiner E, Liu F, Wang B, Larkin C, Agu E, Ghosh S, Semeter J, Simon G, Davis-Martin RE. Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions. Front Psychiatry. 2021 Aug 3;12:707916. doi: 10.3389/fpsyt.2021.707916. PMID: 34413800; PMCID: PMC8369059. Link to article on publisher's site
Frontiers in psychiatry
Boudreaux ED, Rundensteiner E, Liu F, Wang B, Larkin CM, Agu E, Ghosh S, Semeter J, Simon G, Davis-Martin RE. (2021). Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions. Population and Quantitative Health Sciences Publications. https://doi.org/10.3389/fpsyt.2021.707916. Retrieved from https://escholarship.umassmed.edu/qhs_pp/1422
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