Exploring machine learning to predict depressive relapses of bipolar disorder patients

Background: Bipolar disorder (BD) is a chronic mood disorder characterized by recurrent episodes of mania or hypomania and depression, expressed by changes in energy levels and behavior. However, most of relapse studies use evidence-based approaches with statistical methods. With the advance of the precision medicine this study aims to use machine learning (ML) approaches as a possible predictor in depressive relapses in BD. Method: Four accepted and well used ML algorithms (Support Vector Machines, Random Forests, Naive Bayes, and Multilayer Perceptron) were applied to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) dataset in a cohort of 800 patients (507 patients presented depressive relapse and 293 did not), who became euthymic during the study and were followed for one year. Results: The ML algorithms presented reasonable performance in the prediction task, ranging from 61 to 80% in the F-measure. The Random Forest algorithm obtained a higher average of performance (Relapse Group 68%; No Relapse Group 74%). The three most important mood symptoms observed in the relapse visit (Random Forest) were: interest; depression mood and energy. Limitations: Social and psychological parameters such as marital status, social support system, personality traits, might be an important predictor in depressive relapses, although we did not compute this data in our study. Conclusions: Our findings indicate that applying precision medicine models by means of machine learning in BD studies could be feasible as a sensible approach to better support medical decision-making in the BD treatment and prevention of future relapses.