Machine learning model to predict mental health crises from electronic health records

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...

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Detalles Bibliográficos
Autores principales: Garriga, Roger, Mas, Javier, Abraha, Semhar, Nolan, Jon, Harrison, Oliver, Tadros, George, Matic, Aleksandar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205775/
https://www.ncbi.nlm.nih.gov/pubmed/35577964
http://dx.doi.org/10.1038/s41591-022-01811-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205775/
https://www.ncbi.nlm.nih.gov/pubmed/35577964
http://dx.doi.org/10.1038/s41591-022-01811-5

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