Predicting Patient Outcomes from Clinical and Genome-Wide Data

从临床和全基因组数据预测患者结果

• 批准号: 7860710
• 负责人: GREGORY F. COOPER
• 金额: $ 58.26万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7860710
• 关键词: Address; Bayesian Method; Biotechnology; Calibration; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Classification; Clinical; Clinical Data; Computer Simulation; Computer software; Computers; Data; Data Sources; Diabetes Mellitus; Discrimination; Disease; Electronics; Evaluation; Event; Framingham Heart Study; Future; Generations; Genetic; Health Care Costs; Healthcare; Individual; Internet; Link; Longitudinal Studies; Machine Learning; Massachusetts; Methods; Modeling; Outcome; Participant; Patient Care; Patients; Performance; Publishing; Qualifying; Research Personnel; Resource Sharing; Resources; Source Code; Stroke; Testing; Work; base; care systems; clinical care; data sharing; design; genome-wide; improved; insight; novel strategies; predictive modeling

DESCRIPTION (provided by applicant): Clinical classification and prediction are key components of clinical care. Even modest improvements in classification and predictive performance may have significant healthcare consequences in terms of improved patient outcomes and reduced healthcare costs. This proposal describes a new, efficient Bayesian machine-learning method for performing clinical predictions. The method is designed to use both clinical and genome-wide data in predicting patient outcomes. The method's performance will be evaluated using existing clinical and genome-wide data from the Framingham Heart Study that is being made available to qualified researchers through the SHARe resource of the National Center for Biotechnology Information. The outcomes to be predicted in individuals include the onset of major cardiovascular events and death from all causes. The study will evaluate how well these predictions can be made with a new Bayesian method when using traditional clinical data alone, genome-wide data alone, and both types of data together. The method's performance will also be compared that of existing models and methods. The main hypothesis to be tested is that the proposed machine-learning method will be an advancement over existing methods in that it will be computationally feasible to apply it using a combination of traditional clinical data and genome-wide data, and it will yield better predictive performance than do existing predictive models and methods. If shown to be so, this new method is anticipated to provide substantial benefit in future electronic patient-care systems, including applications to computer-based decision support that have available both traditional clinical data and genome-wide data.

描述（由申请人提供）： 临床分类和预测是临床护理的关键组成部分。即使分类和预测性能的适度改进也可能在改善患者结局和降低医疗保健成本方面具有显著的医疗保健后果。该提案描述了一种新的，有效的贝叶斯机器学习方法，用于执行临床预测。该方法旨在使用临床和全基因组数据来预测患者结局。 该方法的性能将使用来自Frachial Heart研究的现有临床和全基因组数据进行评估，该研究通过国家生物技术信息中心的SHARe资源提供给合格的研究人员。在个体中预测的结果包括主要心血管事件的发生和各种原因的死亡。该研究将评估当仅使用传统临床数据、仅使用全基因组数据以及同时使用这两种类型的数据时，使用新的贝叶斯方法进行这些预测的效果。该方法的性能也将比较现有的模型和方法。 要测试的主要假设是，所提出的机器学习方法将是对现有方法的进步，因为使用传统临床数据和全基因组数据的组合来应用它在计算上是可行的，并且它将产生比现有预测模型和方法更好的预测性能。如果证明是这样的话，这种新方法预计将在未来的电子病人护理系统中提供实质性的好处，包括应用于基于计算机的决策支持，既有传统的临床数据和全基因组数据。