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

Wanpracha Chaovalitwong罗格斯大学新不伦瑞克CAREER：与医疗保健和生物技术应用合作的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疾病。由于失控的癫痫给社会带来了巨大的负担，本项目旨在开发一个自动化的癫痫发作预测系统和脑异常活动分类器。为了实现这一目标，基于优化的数据挖掘(DM)方法将被开发来通过脑电(EEG)数据来定量地分析大脑活动。所提出的数据挖掘技术将挖掘脑电中隐藏的模式/关系，这将从系统的角度更好地理解大脑功能(以及其他复杂系统)。具体地说，将基于新的基于优化的DM技术来开发用于癫痫发作预测和脑活动分类的新的DM范例，用于特征选择、聚类和分类。拟议的研究将沿着以下四条路线为计算机科学、工程和医学界做出贡献：(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)实施统计技术以从选定的特征/群中检测模式以预测癫痫发作并对正常和癫痫的脑电活动进行分类，(3)利用检测理论和实验设计来评估和验证所提议的DM范例的有效性、稳健性和不确定性以及微调最佳参数设置，(4)将优化和DM中的基本研究成果扩展到其他跨学科研究，这将为基于优化的数据挖掘和时间序列分析提供一条新的研究途径。这项研究的成功将推进DM优化领域的最新水平，并对医学研究产生重大影响。该方案的研究范围涉及几个新兴的优化和数据挖掘问题，这些问题是由不断增长的计算能力驱动的。该研究对计算机科学、运筹学、计算生物学和物流等多个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、男女、受保护的少数群体，特别是因癫痫而致残的人都能参与。这一建议在癫痫发作预测研究中的成功将缓解这种威胁生命的疾病的痛苦，并提高至少200万美国人(全球1400万人)的生活质量，这些人目前患有癫痫，不分种族、年龄或性别。

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.