A Predictive Analytics Approach to the Optimization of Diagnosis, Treatment, and Ambulatory Management of Major Depressive Disorder and Bipolar Disorder

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Clinicians working with Major Depressive Disorder (MDD) and Bipolar Disorder (BD) patients face multiple challenges, ranging from determining a timely and correct diagnosis and devising an optimal treatment strategy to managing mood episodes in a way that minimizes disease progression and individual suffering. Here, we will now utilize advances in the emerging field of Predictive Analytics in Mental Health to develop, adapt, and implement state-of-the-art machine-learning algorithms directly addressing three most pressing clinical objectives in applied affective disorder research. We aim to construct diagnostic support models 1) differentiating MDD and BD patients, 2) predicting individual response to Electroconvulsive Therapy (ECT) in MDD patients and 3) capable of dynamic real-time relapse-risk prediction based on smartphone data. To this end, we will devise a principled approach dealing with the massively multivariate and multimodal nature of MDD and BD and provide an algorithmic framework aiming to predict real-time relapse-risk. Specifically, we will first develop an unsupervised Deep Learning approach for automated feature-engineering with the goal of representing Magnetic Resonance Imaging data on a lower-dimensional manifold. This will not only alleviate the Curse of Dimensionality, but also provide features which are more invariant to scanner sites and acquisition protocols, enabling the seamless construction of more robust multi-center models. Second, we will employ state-of-the-art data integration methodology to fuse genetic, psychometric, and neuroimaging information, enabling MDD vs. BD classification and ECT response prediction. Building a multimodal model comprising the three most heavily investigated data sources in affective disorder research will – for the first time – provide empirical evidence regarding the question to what extent combining patient characteristics commonly measured in psychiatry improves model performance. Third, we will develop a model for real-time relapse-risk prediction based on smartphone data by evaluating algorithms which conceptualize relapse-risk 1) as a deviation from the patterns present during symptom-free periods and 2) as a critical transition from a Complex Systems perspective. Building on multimodal neuroimaging, genetic and psychometric data already available to us (total N>41,000) as well as on the smartphone-based dataset currently acquired in an associated multicenter project (total N > 2,000), we will fully focus on advancing state-of-the-art machine learning methodology in affective disorders research and facilitate the construction of predictive models with direct relevance for clinical practice. Bringing together the largest datasets available and the most powerful machine learning algorithms emerging today opens up the unique opportunity to catalyze translational efforts in psychiatry, moving the field from proof-of-concept studies towards first clinical applications.