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

III：小：用于抑郁症思想记录知识发现的鲁棒大规模数据挖掘

• 批准号: 1619308
• 负责人: Heng Huang
• 金额: $ 50万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619308&HistoricalAwards=false
• 关键词: III; Small; Robust; Large; Scale

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.

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，以解决在挖掘用于认知行为治疗的大量抑郁症思维记录方面的关键计算挑战。抑郁症正迅速成为我们社会中的主要问题之一，并且还与许多其他健康状况有关，如中风、糖尿病、高血压、HIV/AIDS等。认知行为疗法是抑郁症心理治疗中研究最广泛的形式，而来自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，审查和分析抑郁症的思想记录的过程是非常耗时的，这抑制了临床访谈和新的治疗师的培训。本计画建立一个新颖的资料探勘系统，自动从忧郁症患者的思想记录中发现知识，以协助治疗师选择可能的治疗方法，并协助新治疗师发展认知行为治疗技巧。该项目将促进开发新的教育工具，以启用新课程和加强现有课程。该项目使少数民族学生和得不到充分服务的人口参与研究活动，使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的抑郁症思维记录，本项目探讨了以下研究任务。首先，该项目开发了一个强大的半监督学习模型来分类抑郁症思维记录的逻辑思维错误。第二，本研究探讨一种联合多任务方法，以同时识别抑郁思维记录中的思维错误和情绪的类别。第三，新的多标签和多实例学习研究识别应对活动。第四，通过对多语言抑郁症思维记录的分析，提出了跨语言知识迁移的鲁棒迁移学习方法。同时，设计了并行计算算法，并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程序和自动化抑郁症思想记录数据挖掘，这对智能健康有很大的希望。

项目成果

Semi-Supervised Classifications via Elastic and Robust Embedding

• DOI: 10.1609/aaai.v31i1.10946
• 发表时间: 2017-02
• 作者: Yun Liu;Yiming Guo;Hua Wang;F. Nie;Heng Huang

Asynchronous Mini-Batch Gradient Descent with Variance Reduction for Non-Convex Optimization

• DOI: 10.1609/aaai.v31i1.10940
• 发表时间: 2017-02
• 作者: Zhouyuan Huo;Heng Huang

Joint Capped Norms Minimization for Robust Matrix Recovery

鲁棒矩阵恢复的联合上限范数最小化

• DOI: 10.24963/ijcai.2017/356
• 发表时间: 2017
• 期刊: The 26th International Joint Conference on Artificial Intelligence (IJCAI 2017
• 作者: Nie, Feiping;Huo, Zhouyuan;Huang, Heng
• 通讯作者: Huang, Heng

Learning Task Relational Structure for Multi-task Feature Learning

• DOI: 10.1109/icdm.2016.0166
• 发表时间: 2016-12
• 期刊: 2016 IEEE 16th International Conference on Data Mining (ICDM)
• 作者: De Wang;F. Nie;Heng Huang

Multi-Class Support Vector Machine via Maximizing Multi-Class Margins

• DOI: 10.24963/ijcai.2017/440
• 发表时间: 2017-08
• 作者: Jie Xu;Xianglong Liu;Zhouyuan Huo;Cheng Deng;F. Nie;Heng Huang