A Machine Learning Approach to Identifying Future Suicide Risk
15 Pages Posted: 12 Nov 2018More...
Background It is nearly impossible to predict when someone will die from suicide, but with early intervention many deaths are preventable. Clinical trials using machine learning algorithms have been able to use patient language to compute the likelihood that someone is suicidal at a particular point in time. Here we determine if the language characteristics associated with suicide risk are persistent 30 days after discharge.
Methods Multiple hospital base emergency departments and outpatient clinics were used to enroll subjects (n=253) into one of two groups: suicidal or control. Their responses to standardized instruments and interviews designed to harvest thought markers were recorded and analyzed with machine learning algorithms. Subjects were re-interviewed approximately 30 days later, and the language in their interviews was analyzed to determine the presence of suicidal ideation.
Outcomes The results show that the language characteristics used to classify suicidality at the initial encounter are still present in the participants’ speech 30 days later (AUC = 0·89 (95% CI: 0·85-0·95) with p < 0·0001) and that this also holds in the inverse case; classifiers trained on second interviews could identify the cohort that produced the first interviews (AUC = 0·85 (95% CI: 0·81–0·90) with p < 0·0001).
Interpretation This approach explores the stability of suicidal interviews 30 days after it is recorded. It does so with computational innovations and well established computational linguistic methods. The results show that the thoughts exhibited by a patient’s language are still valid for machine learning 30-days after first disclosed, but the initial correlation with the standard measures is not. This can be useful when seeking decision support for follow-up care.
Funding Statement: Cincinnati Children’s Hospital Medical Center, Innovation Fund.
Declaration of Interests: The authors have no competing interests to declare.
Ethics Approval Statement: A prospective clinical trial was conducted between October 2013 and March 2015 (Institutional Review Board (#2013-3770) approved).
Keywords: Natural Language Processing, Machine Learning, Acoustics, Suicide, Decision Support Techniques
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