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

慢性病是我们医疗保健费用的主要来源，预计这一负担只会增加。除了巨大的经济影响外，它们还经常导致工作效率下降，生活质量显著下降和残疾。从被动到主动的疾病管理的转变对于改善预后和降低成本是必要的，但临床医生几乎没有工具可以帮助预测哪些患者的衰退风险最大。在疾病表现方式存在巨大异质性的情况下，决策尤其具有挑战性。同时，由于电子临床数据存储（如电子健康记录和患者登记）的迅速普及，纵向电子健康数据（EHD）包含了常规临床就诊期间所采取的大量临床测量结果，可以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，可以了解疾病表现方式中个体差异的典型模式，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不能充分利用EHD的丰富性——多样性、复杂性和异质性。该项目提出了一种新的计算框架，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，以跟踪个人的疾病状态。为此，该框架解决了EHD固有的测量过程中的缺失和噪声所带来的挑战。此外，该提案还展示了20年来从大型人口数据库收集的两种不同疾病群体数据的框架。这一建议将显著推进基于现代电子健康数据源的个性化风险预测的计算建模。通过该项目，我们还将培养研究生和博士后，这些人将使用本基金提供的大部分资金；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和结果。有关该项目的更多信息，请参阅该项目的网站：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