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.

Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach.

Background: Bipolar disorder (BD) is a severe psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Predominant polarity (PP) appears to be an important specifier of BD. The present study employed machine learning (ML) algorithms to accurately determine a patient´s PP without the inclusion of number and polarity of past episodes, while exploring associations between PP and demographic and clinical variables.