Precision Medicine for Student Health: Insights from Tsetlin Machines into Chronic Pain and Psychological Distress

Precision medicine has emerged as a critical approach for customizing treatments to individual patients. However, a remarkable gap remains in addressing psychological distress, pain, and their co-occurrence, especially among young adults. By resorting to data from the SHoT2018 study (Student’s Health and Well-Being), a national survey for students pursuing higher education in Norway, we develop a Tsetlin Machine, an interpretable model grounded in propositional logic, to characterize the student population of Norway suffering from these conditions. We leverage the Tsetlin Machine’s transparency to obtain logic rules that explain the classification results. Our results shed light on the link between psychological distress and chronic pain, which are two conditions known to be of mutual magnification. Through analysis of the patterns found by the model, we discover associations that have the potential to mitigate or exacerbate these conditions.