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: 1219639
• 负责人: Wanpracha Chaovalitwongse
• 金额: $ 5.25万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-31 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219639&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.

摘要0546574WANPRACHA CHAOVALITWONGSERUTGERS大学新不伦瑞克省：使用医疗保健和生物技术应用的合作数据挖掘的新型优化方法，迫切需要提高和将定量和定性方法应用于ePilepsy和脑部和大脑疾病的研究。由于不受控制的癫痫病由于与ASS的医疗保健成本对社会造成了重大负担，因此该项目旨在开发自动癫痫发作预测系统和脑部异常活动分类器。为了实现这一目标，将开发基于优化的数据挖掘（DM）方法，以通过电磁图（EEG）数据定量分析大脑活动。提出的DM技术将在系统的角度挖掘隐藏的模式/关系，从而使对大脑功能（以及其他复杂的系统）从系统的角度更加了解。具体而言，将基于新型的基于优化的DM技术，用于FEATURE选择，聚类和分类来开发用于癫痫发作预测和脑活动分类的新的DM范式。拟议的研究将沿以下四行有助于计算机科学，工程和医疗社区：（1）Novel数学模型的开发以及用于DM问题和时间序列分析的数学模型和优化技术，（2）（2）实施统计技术来检测模式，从所选功能/clusters/clusters进行了预测和分类，并评估了正常和ePilect and and and Interionty and the Interionty and eeg Active（3）（3），（3）（3），（3），（3），（3），（3），（3），（3），（3），（3），（3），（3），（3）。验证提出的DM范式的功效，鲁棒性和不确定性，并微调最佳参数设置，（4）扩展了优化和DM的基本研究结果的扩展到其他跨学科研究，该研究将构成基于优化的基于优化的DM和时间序列分析的新途径。拟议的DM和时间序列分析。拟议的研究非常困难地研究了解决决策领域的问题。这项研究的成功将推进DM优化领域的最新技术，并对医学研究产生严重的影响。该提案中的研究范围涉及由越来越多的计算能力驱动的重点新兴优化和DM问题。拟议的研究表明，对包括计算机科学，操作研究，计算生物学和物流在内的许多研究领域产生了广泛的影响。该项目的范围本身将扩大机会，并使所有公民妇女和男女的参与，持续不足的少数群体以及尤其是因癫痫的残疾人而参与。这项提议在发作案例研究中的成功将减轻这种威胁生命的疾病的痛苦，并改善至少200万美国人（全球1400万）的寿命，这些疾病目前正在遭受弗雷梅病，无论种族，年龄或性别如何。