Development and Validation of Computational Algorithms to Assess Kidney Health in Electronic Health Records

项目摘要 这项建议的一个主要目的是为候选人Ozrazgat Baslanti博士提供必要的保护时间， 额外的培训和资源，以发展她在定量方法和理解基础 肾脏疾病进展的机制，并促进她向独立的翻译 卫生保健研究员。长期职业目标是成为一名独立的数据科学家，专注于 急性病的医院护理和护理引起的并发症。本报告的总体目标 应用程序是建立分析方法的基础，用于确定患者的健康轨迹， 急性住院的发作和量化健康状态的转变，可以应用于任何急性 病我们的中心假设是，使用肾脏健康作为这种方法的范例，我们可以确定 住院期间肾脏健康变化的个体状态，使用纵向、高粒度时间 电子健康记录中的数据，确定向更严重的急性和慢性疾病阶段过渡的概率， 肾脏疾病，并提高对影响这些转变的潜在过程的理解。电流 急性肾损伤（AKI）的诊断和风险评估集中于确定AKI发作的严重程度 并且不存在用于评估肾恢复的综合框架。显然缺乏关于 通过非线性和非正态时间估计不同肾脏健康状态之间的转移概率， 依赖域使用纵向电子健康记录数据。基础过程的复杂性 影响从肾脏风险到急性和慢性肾脏疾病更严重阶段的转换概率 需要在足够大和粒度的数据集中应用高级计算模型。具体 该提案的目的是：目的1-扩大和验证大规模肾脏健康的可计算表型 医疗数据。目的2-确定肾脏健康变化的流行病学和临床结果。目标3- 开发并验证概率图形模型，以预测肾脏健康状态的转变， 确定疾病进展的风险因素。拟议的研究是重要的，因为我们将有表型算法 在多中心研究中得到验证的肾脏健康，可以加强机构间的共享， 研究流行病学和肾脏健康变化的结果。这种方法是创新的，因为它 以创新的步骤实施数据科学和统计方面的技术进步， 表型分型算法，其确定肾脏健康变化的可计算表型， 通过非线性和非正常时间预测肾脏健康状态转变的模型， 依赖域使用高粒度的电子健康记录。这将为以下方面的变化奠定基础： AKI患者的护理，通过识别有发生AKI风险的患者， 急性和慢性肾脏疾病。拟议调查完成后，可交付成果将是新的 知识和肾脏健康的诊断和诊断工具。

Project Summary A key aim of this proposal is to equip the candidate, Dr.Ozrazgat Baslanti, with the necessary protected time and additional training and resources to develop her skillset on quantitative methods and understanding of underlying mechanism of progression of kidney disease and facilitate her transition to an independent translational researcher in health care. The long-term career goal is to become an independent data scientist, with a focus on hospital care for acute disease and complications arising from that care. The overall objective of this application is to build the foundation of the analytical approach for identifying patients’ health trajectories during episode of acute hospitalization and quantifying the transitions in health states that can be applied to any acute illness. Our central hypothesis is that using kidney health as a paradigm for this approach we can determine individual states of change in kidney health during hospitalization using longitudinal, highly granular temporal data in electronic health records, determine transition probabilities to more severe stages of acute and chronic kidney disease, and improve understanding of the underlying processes influencing these transitions. Current diagnosis and risk evaluation for acute kidney injury (AKI) are focused on determination of severity of AKI episode and an integrated framework for assessing renal recovery does not exist. There is a clear lack of research on estimating transition probabilities among different states of kidney health through nonlinear and non-normal time- dependent domains using longitudinal electronic health records data. The complexity of underlying processes influencing the transition probabilities from renal risk to more severe stages of acute and chronic kidney disease requires application of advanced computational models in sufficiently large and granular datasets. The specific aims of the proposal are: Aim 1- Expand and validate computable phenotypes of kidney health in large-scale medical data. Aim 2- Determine the epidemiology and clinical outcomes of changes in kidney health. Aim 3- Develop and validate probabilistic graphical models to predict transition through the states of kidney health and identify risk factors for progression. The proposed research is significant as we will have phenotyping algorithms of kidney health, validated in multi-center study, that can enhance their inter-institutional sharing and that enable to study epidemiology and outcomes of changes in kidney health. The approach is innovative because it implements technological advances in data science and statistics in innovative steps to develop and validate a phenotyping algorithm that determines computable phenotypes of changes in kidney health and graphical models to predict transition through the states of kidney health through nonlinear and non-normal time- dependent domains using highly granular electronic health records. This will provide foundation for changes in the care of patients with AKI, through identification of those patients at risk of developing AKI and progressing to acute and chronic kidney disease. On completion of the proposed investigations the deliverables will be new knowledge and a diagnosis and prognostication tool for kidney health.