Machine Learning Applied to Clinical Laboratory Data Predicts Patient-Specific, Near-Term Relapse in Patients in Medication for Opioid Use Disorder Treatment
Paul Pyzowski, Barbara Herbert, Wasim Q. Malik · medRxiv · 2020
Abstract 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.