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

该项目研究新的健壮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量抑郁症思维记录方面的关键计算挑战。抑郁症正迅速成为当今社会的主要问题之一，也与许多其他健康问题有关，如中风、糖尿病、高血压、艾滋病等。认知行为疗法是目前研究最广泛的抑郁症心理疗法形式，而患者的抑郁症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维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。这个项目建立了一个新的数据挖掘系统来自动从抑郁症思维记录中发现知识，以帮助治疗师选择潜在的干预措施，并帮助新的治疗师发展认知行为治疗技能。该项目将促进开发新的教育工具，以支持新课程和改进现有课程。该项目让少数族裔学生和未得到充分服务的人群参与研究活动，让他们更好地接触尖端科学研究。为了有效和高效地分析大规模抑郁思维记录，本项目探索了以下研究任务。首先，该项目开发了一个健壮的半监督学习模型来对抑郁症思维记录中的逻辑思维错误进行分类。第二，该项目研究了一种联合多任务方法，以同时识别抑郁症思维记录中的思维错误和情绪类别。第三，研究了用于识别应对活动的新的多标签和多实例学习方法。第四，为了分析多语言抑郁思维记录，提出了用于跨语言知识迁移的稳健迁移学习方法。同时，设计了并行计算算法，并将其应用于大规模抑郁症思维记录数据挖掘。这些新颖的数据挖掘算法旨在解决大规模应用和自动化抑郁症思维记录数据挖掘，这为智能健康带来了巨大的希望。

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.