Predictive Modelling of Susceptibility to Substance Abuse, Mortality and Drug-Drug Interactions in Opioid Patients - Vunikili, Ramya; Glicksberg, Benjamin S.; Johnson, Kipp W.; Dudley, Joel T.; Subramanian, Lakshminarayanan; Shameer, Khader (Frontiers in Artificial Intelligence, 2021)
Opioids are a class of drugs that are known for their use as pain relievers. They bind to opioid receptors on nerve cells in the brain and the nervous system to mitigate pain. Addiction is one of the chronic and primary adverse events of prolonged usage of opioids. They may also cause psychological disorders, muscle pain, depression, anxiety attacks, etc. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse and mortality by using their prescription histories. Also, we discover particularly threatening drug-drug interactions in the context of opioid usage. Using a publicly available dataset from MIMIC-III, we trained two models, Logistic Regression with L2 regularization (baseline) and Extreme Gradient Boosting (enhanced model), to classify the patients of interest into two categories on their susceptibility to opioid abuse. We’ve also used K-Means clustering, an unsupervised algorithm, to explore drug-drug interactions that might be of concern. The baseline model for classifying patients susceptible to opioid abuse has an accuracy of 77.67%, while the enhanced model has an accuracy of 93.59%. These models can be used as a preliminary step towards inferring the causal effect of opioid usage and can help monitor the prescription practices to minimize opioid abuse. Our results suggest that the enhanced model provides a promising approach in preemptive identification of patients at risk for opioid abuse. By discovering and correlating the patterns contributing to opioid overdose or abuse among various patients, machine learning models can be used as an efficient tool to help uncover the existing gaps and/or fraudulent practices in prescription writing. To quote an example of one such incidental finding, our study discovered that insulin might be interacting with opioids in an unfavorable way leading to complications in diabetic patients. This observation backs up prior research studies done on a similar aspect. To increase the translational value of our work, the predictive models and the associated software code are made available under the MIT License.