Predictive Machine Learning for Personalised Medicine in Major Depressive Disorder

Depression is a common psychiatric disorder with substantial recurrence risk. Accurate prediction from easily collected data would aid in diagnosis, treatment and prevention. We used machine learning in the Generation Scotland cohort to predict lifetime risk of depression and, among cases, recurrent depression. Rank aggregation was used to combine results across ten different algorithms and identify highly predictive variables. The model containing all but the cardiometabolic predictors had the highest predictive ability on independent data. Rank aggregation produced a reduced set of predictors without decreasing predictive performance (lifetime: 20 out of 154 predictors and Receiver Operating Characteristic area under the curve (AUC)=0·84, recurrent: 10 out of 180 predictors and AUC=0·76). Here we develop a pipeline which leads to a small set of highly predictive variables. This information can be easily collected with a smartphone ‘application’ to help diagnosis and treatment, while longitudinal tracking may help patients in self-management. Significance Depression is the most common psychiatric disorder and a leading cause of disability worldwide. Patients are often diagnosed and treated by non-specialist clinicians who have limited time available to assess them. We present a novel methodology which allowed us to identify a small set of highly predictive variables for a diagnosis of depression, or recurrent depression in patients. This information can easily be collected using a tablet or smartphone application in the clinic to aid diagnosis.