SCH: INT: Collaborative Research: Modeling Disease Trajectories in Patients with Complex, Multiphenotypic Conditions

SCH：INT：合作研究：对复杂、多表型病症患者的疾病轨迹进行建模

• 批准号: 1418590
• 负责人: Suchi Saria
• 金额: $ 139.19万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1418590&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Modeling

Chronic conditions are driving the majority of our health care costs, and this burden is only expected to rise. Beyond the enormous economic impact, they often lead to loss of work productivity, marked decrease in quality of life and disability. A shift from reactive to proactive management of the disease is necessary to improve outcomes and reduce costs, yet clinicians have few tools at their disposal that can help prognosticate which patients are at greatest risk for decline. Decision making is particularly challenging in conditions where large heterogeneity is present in the way the disease might present itself. Simultaneously, due to the rapid proliferation of electronic clinical data stores such as Electronic Health Records and Patient Registries, longitudinal electronic health data (EHD), containing the multitude of clinical measurements taken during routine clinical visits, are becoming available at scale for retrospective analysis. These data provide an unprecedented opportunity to learn about canonical patterns of variability between individuals in the way a disease manifests, and develop novel approaches for individualizing risk prediction. Traditional clinical risk prediction tools do not exploit the richness -- the diversity, complexity and heterogeneity -- of EHD. This project proposes a novel computational framework for individualized risk prediction from modern electronic health data sources.This project develops a flexible Bayesian framework for jointly modeling the array of complex measurements present in the electronic health record to track an individual's disease status over time. Towards this, the framework addresses challenges due to missingness and noise in the measurement process, inherent in EHD. In addition, this proposal demonstrates the framework on data collected from large scale population databases over two decades for two different disease groups. This proposal will significantly advance computational modeling for individualized risk prediction from modern electronic health data sources. Through this project, we will also train graduate students and postdoctoral fellows, on whom the majority of the funds provided in this grant are being used; with our rapidly growing healthcare budget, the US is in need for engineers and computational methodologists who can devise new ways to improve and optimize our healthcare utilization and outcomes. For further information on this project see the project web site: http://www.cs.jhu.edu/~ssaria/individualizedRiskPrediction.html

慢性病正在推动我们的大部分医疗保健费用，预计这一负担只会上升。除了巨大的经济影响外，它们还往往导致工作生产力的损失、生活质量的显著下降和残疾。从反应性到主动性的疾病管理的转变是必要的，以改善结果和降低成本，但临床医生几乎没有工具可以帮助他们的处置，可以帮助明确哪些患者是在下降的风险最大。在疾病本身可能存在巨大异质性的情况下，决策尤其具有挑战性。同时，由于电子健康记录和患者登记等电子临床数据存储的快速增长，包含在常规临床访视期间进行的大量临床测量的纵向电子健康数据（EHD）正在大规模地用于回顾性分析。这些数据提供了一个前所未有的机会来了解疾病表现方式中个体之间的典型变异模式，并开发个性化风险预测的新方法。传统的临床风险预测工具没有利用EHD的丰富性--多样性、复杂性和异质性。本项目提出了一个新的计算框架，用于从现代电子健康数据源进行个性化风险预测。本项目开发了一个灵活的贝叶斯框架，用于联合建模电子健康记录中存在的复杂测量阵列，以跟踪个人随时间的疾病状态。为此，该框架解决了由于EHD固有的测量过程中的缺失和噪声而带来的挑战。此外，该提案还展示了20年来从两个不同疾病组的大规模人口数据库中收集的数据框架。这一提议将大大推进从现代电子健康数据源进行个性化风险预测的计算建模。通过这个项目，我们还将培训研究生和博士后研究员，在这个赠款中提供的大部分资金正在使用;随着我们快速增长的医疗保健预算，美国需要工程师和计算方法学家，他们可以设计新的方法来改善和优化我们的医疗保健利用率和结果。 有关该项目的更多信息，请访问项目网站：http://www.cs.jhu.edu/~ssaria/individualizedRiskPrediction.html