A new study led by Drew Wilimitis, statistical analyst in the lab, is out now in JAMA Network Open. Co-authors include our wonderful collaborators Robert Turer, MD, MS; Michael Ripperger; Allison McCoy, PhD; Sarah Sperry, PhD; Elliot Fielstein, PhD; and Troy Kurz, MD.
In this study, we asked: what happens when we combine automated risk models with face-to-face screening by clinicians for suicide prevention? Suicide risk prediction was optimal when leveraging both in-person screening (for acute measures of risk in patient-reported suicidality) and historical EHR data (for underlying clinical factors that can quantify a patient’s passive risk level). We found that to improve suicide risk classification, prediction systems could combine pretrained machine learning with structured clinician assessment without needing to retrain the original model.