From everyday life predictions to suicide prevention: Clinical and ethical considerations in suicide predictive analytic tools

Abstract

Advances in artificial intelligence and machine learning have fueled growing interest in the application of predictive analytics to identify high‐risk suicidal patients. Such application will require the aggregation of large‐scale, sensitive patient data to help inform complex and potentially stigmatizing health care decisions. This paper provides a description of how suicide prediction is uniquely difficult by comparing it to nonmedical (weather and traffic forecasting) and medical predictions (cancer and human immunodeficiency virus risk), followed by clinical and ethical challenges presented within a risk‐benefit conceptual framework. Because the misidentification of suicide risk may be associated with unintended negative consequences, clinicians and policymakers need to carefully weigh the risks and benefits of using suicide predictive analytics across health care populations. Practical recommendations are provided to strengthen the protection of patient rights and enhance the clinical utility of suicide predictive analytics tools.

Evaluating the clinical utility of an easily applicable prediction model of suicide attempts, newly developed and validated with a general community sample of adults

“…Clinically, these low thresholds emphasize the benefit of capturing true positive individuals over the cost of capturing false positive individuals. This trade-off eventually raises legal, ethical, and economic concerns [52], which are beyond the scope of this report. Methodologically, thresholds that are close to, as opposed to being distant from, the outcome rate in the study sample, render the prediction model less sensitive to model miscalibration [49].…”