CAREER: Novel Optimization Methods for Cooperative Data Mining with Healthcare and Biotechnology Applications

职业：医疗保健和生物技术应用中协作数据挖掘的新颖优化方法

• 批准号: 0546574
• 负责人: Wanpracha Chaovalitwongse
• 金额: $ 40万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0546574&HistoricalAwards=false
• 关键词: CAREER; Novel; Optimization; Methods; Cooperative

ABSTRACT0546574Wanpracha ChaovalitwongseRutgers University New BrunswickCAREER: NOVEL OPTIMIZATION METHODS FOR COOPERATIVE DATA MINING WITH HEALTH-CARE AND BIOTECHNOLOGY APPLICATIONSThere is an urgent need to advance and apply quantitative and qualitative approaches to the study ofepilepsy and brain disorders. As uncontrolled epilepsy poses a significant burden to society due to as-sociated healthcare cost, this project is aimed at the development of an automated seizure predictionsystem and brain abnormal activity classifier. To achieve this goal, optimization-based data mining(DM) approaches will be developed to quantitatively analyze the brain activity through electroen-cephalogram (EEG) data. The proposed DM techniques will excavate hidden patterns/relationshipsin EEGs, which will give a greater understanding of brain functions (as well as other complex sys-tems) from a system perspective. Specifically, a new DM paradigm for the seizure prediction andbrain activity classification will be developed based on novel optimization-based DM techniques forfeature selection, clustering, and classification. The proposed research will contribute to the computerscience, engineering and medical communities along the following four lines: (1) the development ofnovel mathematical models and optimization techniques for DM problems and time series analysis,(2) the implementation of statistical techniques to detect patterns from selected features/clustersfor predicting seizures and classifying normal and epileptic EEG activity, (3) the utility of detectiontheory and the experimental designs to assess and validate the efficacy, robustness, and uncertaintyof the proposed DM paradigm as well as fine-tune the optimal parameter setting, (4) the extensionof the fundamental research findings in optimization and DM to other cross-disciplinary research,which will constitute a new avenue of research in optimization-based DM and time series analysis.The proposed research is very crucial to decision making processes in real world problems. Successof this research will advance the state-of-the-art in the field of optimization in DM, and have agreatly significant impact on medical research. The research scope in this proposal touches uponseveral emerging optimization and DM problems, which are driven by ever growing computationalpower. The proposed research has shown a broad impact on many research fields including computerscience, operations research, computational biology, and logistics. The scope of this project itselfwill broaden opportunities and enable the participation of all citizens women and men, underrep-resented minorities, and especically persons disabled by epilepsy. Success of this proposal in seizureprediction research will relieve the anguish from this life-threatening disease and improve the lifequality of at least 2 million Americans (14 millions worldwide), who are currently suffering fromepilepsy regardless of race, age, or gender.

Wanpracha Chaovalitwongse Rutgers University New BrunswickCareer：Novel Optimization Methods FOR Cooperative Data Mining WITH Health Care AND Biotechnology Applications（用于医疗保健和生物技术应用的合作数据挖掘的新优化方法）迫切需要推进和应用定量和定性方法来研究癫痫和脑部疾病。由于无法控制的癫痫由于相关的医疗费用而对社会造成重大负担，因此本项目旨在开发自动癫痫预测系统和大脑异常活动分类器。为了实现这一目标，将开发基于优化的数据挖掘（DM）方法，通过脑电图（EEG）数据定量分析大脑活动。提出的数据挖掘技术将挖掘隐藏的模式/关系在EEG，这将提供一个更好的理解大脑功能（以及其他复杂系统）从系统的角度来看。具体来说，将基于用于特征选择、聚类和分类的新型基于优化的DM技术开发用于癫痫发作预测和大脑活动分类的新DM范式。拟议的研究将有助于计算机科学，工程和医学界沿着以下四条线：（1）为DM问题和时间序列分析开发新的数学模型和优化技术，（2）实施统计技术，从选定的特征/聚类中检测模式，用于预测癫痫发作和对正常和癫痫EEG活动进行分类，（3）利用检测理论和实验设计来评估和验证所提出的DM范式的有效性、鲁棒性和不确定性，以及微调最佳参数设置。（4）将最优化和DM的基础研究成果扩展到其他跨学科研究，这为基于优化的数据挖掘和时间序列分析提供了一条新的研究途径，对真实的世界问题的决策过程具有重要意义。本研究的开展将推动DM优化领域的发展，对医学研究产生重大影响。在这个建议的研究范围触及几个新兴的优化和DM问题，这是由不断增长的计算能力。这项研究对计算机科学、运筹学、计算生物学和物流等许多研究领域产生了广泛的影响。该项目本身的范围将扩大机会，使所有公民-妇女和男子、未得到充分代表的少数群体，特别是因癫痫而残疾的人-都能参与。这项提案在癫痫预测研究中的成功将减轻这种危及生命的疾病的痛苦，并改善至少200万美国人（全球1400万人）的生活质量，他们目前正在遭受癫痫病的折磨，无论种族，年龄或性别。