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

慢性疾病正在推动我们的大部分医疗保健成本，而且这一负担预计只会上升。除了巨大的经济影响外，它们还往往导致工作生产力的丧失、生活质量的显著下降和残疾。从被动到主动的疾病管理对于改善结果和降低成本是必要的，但临床医生几乎没有可用的工具来帮助预测哪些患者面临最大的下降风险。在疾病可能出现的方式存在很大异质性的情况下，决策尤其具有挑战性。与此同时，由于电子健康记录和患者登记簿等电子临床数据存储的快速增长，包含常规临床访问期间进行的大量临床测量的纵向电子健康数据(EHD)正变得大规模可用，用于回顾分析。这些数据提供了一个前所未有的机会，可以了解疾病表现方式中个体之间的典型变异性模式，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--。这个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。该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一系列复杂测量，以跟踪个人随时间的疾病状态。为此，该框架解决了由于测量过程中的遗漏和噪声而导致的挑战，这是EHD固有的。此外，该提案还展示了从大规模人口数据库中收集的两种不同疾病组20年来的数据框架。这一建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，这笔赠款中提供的大部分资金都用于他们身上；随着我们的医疗预算快速增长，美国需要能够设计出新方法来改善和优化我们的医疗保健利用和结果的工程师和计算方法学家。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

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