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Computationally Scalable and Clinically Sound: Laying the Groundwork to Use Machine Learning Techniques for Social Media and Language Data in Predicting Psychiatric Symptoms

      Machine learning approaches to mental health face a challenging tension between scalability and validity. Tools are needed to help predict symptoms, but important uncertainties remain. How can we be confident that remote data surveillance reflects an individual’s true clinical condition? How do we obtain such data at a large scale for machine learning techniques? We present work aimed at addressing these gaps.
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