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A schizophrenia relapse has severe consequences for a patient's health, work,and sometimes even life safety. If an oncoming relapse can be predicted ontime, for example by detecting early behavioral changes in patients, theninterventions could be provided to prevent the relapse. In this work, weinvestigated a machine learning based schizophrenia relapse prediction modelusing mobile sensing data to characterize behavioral features. Apatient-independent model providing sequential predictions, closelyrepresenting the clinical deployment scenario for relapse prediction, wasevaluated. The model uses the mobile sensing data from the recent four weeks topredict an oncoming relapse in the next week. We used the behavioral rhythmfeatures extracted from daily templates of mobile sensing data, self-reportedsymptoms collected via EMA (Ecological Momentary Assessment), and demographicsto compare different classifiers for the relapse prediction. Naive Bayes basedmodel gave the best results with an F2 score of 0.083 when evaluated in adataset consisting of 63 schizophrenia patients, each monitored for up to ayear. The obtained F2 score, though low, is better than the baselineperformance of random classification (F2 score of 0.02 $\pm$ 0.024). Thus,mobile sensing has predictive value for detecting an oncoming relapse and needsfurther investigation to improve the current performance. Towards that end,further feature engineering and model personalization based on the behavioralidiosyncrasies of a patient could be helpful.

Patient-independent Schizophrenia Relapse Prediction Using Mobile Sensor based Daily Behavioral Rhythm Changes