New Paper – Risky business: a scoping review for communicating results of predictive models between providers and patients

Given widespread excitement around predictive analytics and the proliferation of machine learning algorithms that predict outcomes, a key next step is understanding how this information is — or should be — communicated with patients. In collaboration with Mollie McKillop, Patricia Lee, Joyce Harris, Christopher Simpson, and Laurie Novak, we conducted a scoping review to identify…

New Paper – Ensemble learning to predict opioid-related overdose using statewide prescription drug monitoring program and hospital discharge data in the state of Tennessee

Over 2,000 Tennesseans died in 2019 from overdose – can we predict overdose risk anywhere in the state to guide prevention? Our study on this question is out now in the Journal of the American Medical Informatics Association. Since 2012, Tennessee has participated in a comprehensive Prescription Drug Monitoring Program called the Controlled Substance Monitoring…

New Project – Predicting and Preventing Suicide in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) in the COVID Era

We are pleased to announce a new study beginning in July 2021 which aims to better understand suicidality in patients living with ME/CFS. ME/CFS is a debilitating chronic illness, and previous studies have found suicide risk in those with ME/CFS to be seven times greater than the general population. This study aims to predict and…