UMMS Affiliation

Department of Psychiatry

Publication Date

2020-02-20

Document Type

Article

Disciplines

Artificial Intelligence and Robotics | Epidemiology | Health Information Technology | Psychiatry | Psychiatry and Psychology | Translational Medical Research

Abstract

Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Advanced machine-learning algorithms and deep neural networks were utilized to build models with the data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-level analysis in order to aid in the interpretation of the results for health-care providers managing the at-risk cohorts. The 1-year suicide attempt risk model attained an area under the curve (AUC ROC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the "very high risk" category was 60 times greater than the population baseline when tested in the prospective cohorts. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socioeconomic determinants were recognized as significant features associated with incident suicide attempt.

Keywords

suicide, suicide attempts, early warning system, risk model

Rights and Permissions

Copyright © The Author(s) 2020. Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

DOI of Published Version

10.1038/s41398-020-0684-2

Source

Zheng L, Wang O, Hao S, Ye C, Liu M, Xia M, Sabo AN, Markovic L, Stearns F, Kanov L, Sylvester KG, Widen E, McElhinney DB, Zhang W, Liao J, Ling XB. Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records. Transl Psychiatry. 2020 Feb 20;10(1):72. doi: 10.1038/s41398-020-0684-2. PMID: 32080165; PMCID: PMC7033212. Link to article on publisher's site

Journal/Book/Conference Title

Translational psychiatry

Comments

Full author list omitted for brevity. For the full list of authors, see article.

Related Resources

Link to Article in PubMed

PubMed ID

32080165

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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