Project Summary Preventing suicide is one of the great public health challenges facing the US health care system. People who seek emergency care for mental health complaints are at high short-term risk of non-fatal suicide events and suicide. Yet identifying high-risk patients is challenging as risk fluctuates in a poorly understood manner. It is especially difficult to evaluate risk in emergency settings, where access to the patient's mental health history is often limited. The proposed project seeks to address this critical knowledge gap by pairing data mining and machine learning methods with rich data sources in order to develop short-term prediction models of non-fatal suicidal events and suicide for patients presenting to EDs with mental health problems. The specific aims of this study are to 1) apply advanced machine learning data analytic techniques to electronic health record (EHR) data to develop a clinically rich description of ED mental health patient characteristics that predict suicide and non-fatal suicidal events over a 90- day follow-up period; 2) use longitudinal and temporal features of EHR and claims data from the 180 days preceding the ED mental health visit to generate clinically interpretable suicide and suicidal event risk scores; and 3) convene ED physicians to enhance model development, clinical interpretability, and utility of a suicide risk assessment clinical decision support tool. We will achieve these aims by leveraging several different sophisticated machine learning analytic methods of existing longitudinal clinical and service use information. We seek to develop point-in-time, short-term risk scores for suicidal symptoms and suicide death and the clinical features that drive that risk that may be used to inform clinical risk assessment and management of patients who present to EDs with mental health complaints. Risk algorithms will be developed and validated using health information from a large combined EHR and claims dataset with over 24 million commercially insured patients, which is linked to the National Death Index. Findings will yield new insights regarding patient-specific risk factors and potential targets for intervention. By drawing on data sources common to most health care systems and using efficient computer algorithms this approach has the potential to develop clinically interpretable suicide risk scores at the point of ED evaluation and following disposition. This will help front- line clinicians focus their efforts on high risk patients during high risk periods to inform intervention decisions about suicide risk.