Predicting Suicide Risk with Machine Learning

Using anonymized data from nearly 3 million patients and almost 20 million visits, researchers from Kaiser Permanente, HealthPartners, and Henry Ford Health System developed a predictive model that more accurately identifies patients at risk of suicide than existing assessments.

The new model uses data from Epic, including results from depression assessment questionnaires, indications of substance abuse, prescriptions for psychiatric medications, and other documentation. Critical to all this was machine learning, which was used to recognize patterns that help analyze indicators corresponding to suicide risk………

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Predicting Suicide Risk with Machine Learning

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