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The ubiquity of smartphones in modern life suggests the possibility to use them to continuously monitor patients, for instance to detect undiagnosed diseases or track treatment progress. Such data collection and analysis may be especially beneficial to patients with i.) mental disorders, as these individuals can experience intermittent symptoms and impaired decision-making, which may impede diagnosis and care-seeking, and ii.) progressive neurological diseases, as real-time monitoring could facilitate earlier diagnosis and more effective treatment. This paper presents a new method of leveraging passively-collected smartphone data and machine learning to detect and monitor brain disorders such as depression and Parkinson’s disease. Crucially, the algorithm is able learn accurate, interpretable models from small numbers of labeled examples (i.e., smartphone users for whom sensor data has been gathered and disease status has been determined). Predictive modeling is achieved by learning from both real patient data and ‘synthetic’ patients constructed via adversarial learning. The proposed approach is shown to outperform state-of-the-art techniques in experiments involving disparate brain disorders and multiple patient datasets. |
