Machine learning model to predict mental health crises from electronic health records
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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.
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Evaluative studies in applied settings are limited and primarily focus on predicting outcomes in care, not on improving provider-client fit from the start of care.
“Although predictive models show promise for improving healthcare access and quality,23 evaluative studies in applied settings are limited and primarily focus on predicting outcomes in care, not on improving provider-client fit from the start of care.24-26”
From Using Machine Learning to Match Clients and Therapy Providers: Evaluating Clinical Quality and Cost of Care (2025) · cites this paper as [26]
The Methods (Study design and setting and Clinical evaluation; page numbers not provided) describe an evaluation in routine care of a model predicting crises among patients already on clinical teams’ caseloads. This supports the paper as an example of outcome prediction during care rather than provider–client matching at the start of care. It does not establish that applied evaluations are limited or that they primarily have this focus. (LLM, conf=0.85)
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“…enhance the prediction of human mental readiness and performance by analyzing complex patterns of large neuro-psycho-physiological datasets, operational context and human performance during startle operations (Chen et al., 2022; Garriga et al., 2022; Le Glaz et al., 2021; Sabry et al., 2022).”
- Authors
- Garriga, R., Mas, J., Abraha, S., Nolan, J., Harrison, O., Tadros, G., & Matic, A.
- Affiliations
- Universitat Pompeu Fabra (ES)
- Birmingham and Solihull Mental Health NHS Foundation Trust (GB)
- Aston University (GB)
- Corresponding author
- Garriga, Roger
- Matic, Aleksandar
- Venue
- Nature Medicine
- Published
- 2022-05-16
- Volume
- 28
- Issue
- 6
- Pages
- 1240-1248
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC9205775
- OpenAlex
- W4280536523
- Cited by
- 229
- References
- 56
- Citations by year
- 2026: 56 · 2025: 90 · 2024: 46 · 2023: 33 · 2022: 4
- FWCI
- 14.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Medicine (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Aston Publications Explorer (Aston University) (acceptedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- Funders
- Health Foundation (709246)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- electronic health records, risk prediction, clinical decision support, continuous patient monitoring, clinical utility, mental health services
- MeSH terms
- Machine Learning, Humans, Mental Health, Prospective Studies, ROC Curve, Electronic Health Records
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