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.
Congratulations to Drew for leading his first biomedical publication! Read the open access JAMA Network Open manuscript here. See coverage of our study in the VUMC Reporter here.