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

该项目研究了新的强大的大规模数据挖掘和机器学习算法，以解决在为认知行为治疗挖掘大规模抑郁症思想记录方面的关键计算挑战。抑郁症正迅速成为我们社会的主要问题之一，并且还与许多其他健康状况有关，如中风、糖尿病、高血压、艾滋病毒/艾滋病等。认知行为疗法是目前研究最广泛的抑郁症心理治疗形式，患者的抑郁思维记录是认知行为疗法的关键组成部分。然而，回顾和分析抑郁症思想记录的过程非常耗时，这既阻碍了临床访谈，也阻碍了新治疗师的培训。本项目建立了一个新的数据挖掘系统，从抑郁症的思想记录中自动发现知识，以帮助治疗师选择潜在的干预措施，并帮助新治疗师发展认知行为治疗技能。这项计划将有助开发新的教育工具，以开办新课程及加强现有课程。该项目让少数民族学生和服务不足的人群参与研究活动，让他们更好地接触前沿科学研究。为了有效、高效地分析大范围的抑郁症思想记录，本项目探索了以下研究任务。首先，该项目开发了一个鲁棒的半监督学习模型，用于对抑郁症思维记录的逻辑思维错误进行分类。其次，研究了一种同时识别抑郁症思维记录中思维错误和情绪类别的联合多任务方法。第三，研究了新的多标签多实例学习识别应对活动。第四，分析多语言抑郁症患者的思维记录，建立稳健的跨语言知识迁移学习方法。同时，设计了并行计算算法，并将其应用于大规模洼地思想记录数据挖掘。这些新颖的数据挖掘算法旨在解决大规模应用，实现抑郁症思想记录数据挖掘的自动化，为智能健康提供了广阔的前景。

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.