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Machine learning model to predict mental health crises from electronic health records
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Garriga, Roger, Mas, Javier, Abraha, Semhar, Nolan, Jon, Harrison, Oliver, Tadros, George and Matic, Aleksandar (2022) Machine learning model to predict mental health crises from electronic health records. Nature Medicine, 28 (6). pp. 1240-1248. doi:10.1038/s41591-022-01811-5 ISSN 1078-8956.
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Official URL: http://dx.doi.org/10.1038/s41591-022-01811-5
Abstract
The timely identification of patients who are at risk of a mental health crisis can lead to improved outcomes and to the mitigation of burdens and costs. However, the high prevalence of mental health problems means that the manual review of complex patient records to make proactive care decisions is not feasible in practice. Therefore, we developed a machine learning model that uses electronic health records to continuously monitor patients for risk of a mental health crisis over a period of 28 days. The model achieves an area under the receiver operating characteristic curve of 0.797 and an area under the precision-recall curve of 0.159, predicting crises with a sensitivity of 58% at a specificity of 85%. A follow-up 6-month prospective study evaluated our algorithm’s use in clinical practice and observed predictions to be clinically valuable in terms of either managing caseloads or mitigating the risk of crisis in 64% of cases. To our knowledge, this study is the first to continuously predict the risk of a wide range of mental health crises and to explore the added value of such predictions in clinical practice.
Item Type: | Journal Article | ||||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics R Medicine > RA Public aspects of medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School | ||||||||||
Library of Congress Subject Headings (LCSH): | Mental health , Mental illness , Mental illness -- Prevention , Medical records -- Data processing, Machine learning , Data mining -- Health aspects | ||||||||||
Journal or Publication Title: | Nature Medicine | ||||||||||
Publisher: | Nature Publishing Group | ||||||||||
ISSN: | 1078-8956 | ||||||||||
Official Date: | June 2022 | ||||||||||
Dates: |
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Volume: | 28 | ||||||||||
Number: | 6 | ||||||||||
Page Range: | pp. 1240-1248 | ||||||||||
DOI: | 10.1038/s41591-022-01811-5 | ||||||||||
Status: | Peer Reviewed | ||||||||||
Publication Status: | Published | ||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||
Date of first compliant deposit: | 13 July 2022 | ||||||||||
Date of first compliant Open Access: | 13 July 2022 | ||||||||||
RIOXX Funder/Project Grant: |
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