On Predicting Relapse in Schizophrenia using Mobile Sensing in a Randomized Control Trial

摘要

Schizophrenia is a severe psychiatric disorder. We use the CrossCheck study dataset to develop methods to predict whether or not a patient with schizophrenia is going to relapse from mobile phone data. Out of 75 patients in the year long randomized controlled trial only 27 relapse episodes occur. We apply various techniques to address predicting rare events in a longitudinal dataset. We apply resampling methods combining oversampling relapse examples and undersampling non-relapse examples and impute missing data. To avoid overfitting, we apply feature selection and transformation (i.e., PCA) to reduce the feature dimensionality. We find the best relapse prediction result using the first 100 principal components from both passive sensing and self-reports with 30-day prediction windows (precision=26.8%, recall=28.4%). If we demand the recall to be greater than 50%, we find the best result using 25 principle components from both passive sensing and self-reports with 30-day prediction windows (precision=15.4%, recall=51.6%).