III: Small: Robust Large-Scale Data Mining for Knowledge Discovery in Depression Thought Records

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This project investigates new robust large-scale data mining and machine learning algorithms to solve critical computational challenges in mining massive depression thought records for cognitive behavior therapy. Depression is rapidly emerging as one of the major problems in our society and is also related to many other health conditions, such as stroke, diabetes, hypertension, HIV/AIDS, etc. Cognitive behavior therapy is the most extensively researched form of psychotherapy for depression, and the depression thought records from patients is the key component of cognitive behavior therapy. However, the process of reviewing and analyzing the depression thought records is extremely time consuming, which inhibits both clinical interviews and the training of new therapists. This project builds a novel data mining system to automatically discover knowledge from depression thought records for assisting therapists in selecting potential interventions and aiding new therapists in their development of cognitive behavior therapy skills. This project will facilitate the development of novel educational tools to enable new courses and enhance current courses. This project engages minority students and under-served populations in research activities to give them a better exposure to cutting-edge science research. To effectively and efficiently analyze large-scale depression thought records, this project explores the following research tasks. First, the project develops a robust semi-supervised learning model to categorize logical thinking errors of depression thought records. Second, the project investigates a joint multi-task method to simultaneously recognize the categories of thinking errors and emotions of depression thought records. Third, new multi-label and multi-instance learning is studied for identifying coping activities. Fourth, to analyze the multi-language depression thought records, robust transfer learning methods are developed for cross-language knowledge transfer. Meanwhile, parallel computational algorithms are designed and applied for large-scale depression thought record data mining. These novel data mining algorithms are designed to solve large-scale applications and automate the depression thought record data mining, which holds great promise for smart health.