Predictive Analytics for Opioid Use Disorder

Monday, August 17, 2020

Our new paper on predictive analytics in opioid use disorders is available! We have developed a method to predict relapse in a time window when a clinical intervention can be made. As far as we know this is the first quantitative method using only clinical data to predict imminent, patient-specific relapse, during an outpatient visit and when a clinical intervention can be made.

The full paper can be accessed HERE.

Machine Learning Applied to Clinical Laboratory Data Predicts Patient-Specific, Near-Term Relapse in Patients in Medication for Opioid Use Disorder Treatment

We have developed a data-driven, algorithmic method for identifying patients in an out- patient buprenorphine program at high risk for relapse in the following seven days. This method uses data already available in clinical laboratory data, can be made available in a timely matter, and is easily understandable and actionable by clinicians. Use of this method could significantly reduce the rate of relapse in addiction treatment programs by targeting interventions at those patients most at risk for near term relapse.