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

电子健康记录中评估肾脏健康的计算算法的开发和验证

基本信息

批准号：
10890956
负责人：
Tezcan Ozrazgat Baslanti
金额：
$ 6.39万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2024-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10890956
关键词：
Acute Acute Disease Acute Renal Failure with Renal Papillary Necrosis Admission activity Algorithms Award Bayesian Network Caring Chronic Kidney Failure Clinical Clinical Data Complex Complication Computational algorithm Computer Models Data Data Science Data Scientist Data Set Decision Making Development Diagnosis Dimensions Electronic Health Record Electronics Epidemiology Evaluation Foundations Goals Guidelines Health Healthcare Hospitalization Individual Institution Investigation Kidney Kidney Diseases Knowledge Medical Mentors Methods Mission Modeling Multicenter Studies Natural History Outcome Patient Care Patient-Focused Outcomes Patients Phenotype Physiological Preventive therapy Probability Process Records Recovery Renal function Research Resources Risk Risk Factors Series Severities Technology Testing Time Training Training Programs Treatment Protocols United States National Institutes of Health Validation adjudication adverse outcome career computable phenotypes data repository epidemiology study hospital care improved improved outcome innovation inter-institutional machine learning method mortality phenotyping algorithm predictive tools prognostication skills statistical and machine learning statistics therapy development tool translational scientist

项目摘要

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.

项目摘要该提案的关键目的是为候选人ozrazgat Baslanti Dr. 额外的培训和资源以发展定量方法的技能和对基础的理解肾脏疾病进展的机制，并促进她向独立翻译过渡卫生保健研究人员。长期职业目标是成为一名独立数据科学家，重点医院护理急性疾病和由此护理引起的并发症。总体目标应用是为识别患者的健康轨迹的分析方法的基础急性住院的发作并量化可以应用于任何急性的健康状态中的过渡疾病。我们的中心假设是，将肾脏健康作为这种方法的范式，我们可以确定使用纵向，高度颗粒状临时的住院期间肾脏健康变化的个别状态电子健康记录中的数据，确定过渡可能性到更严重的急性和慢性阶段肾脏疾病，并改善对基本过程的理解会影响这些转变。当前的急性肾脏损伤（AKI）的诊断和风险评估重点是确定AKI发作的严重程度并且不存在用于评估肾脏回收的综合框架。显然缺乏研究通过非线性和非正常时间 - 使用纵向电子健康记录数据的依赖域。基础过程的复杂性影响从肾风险到更严重急性和慢性肾脏疾病的过渡可能性需要在足够大的颗粒数据集中应用高级计算模型。具体该提案的目的是：目标1-大规模扩展和验证可计算的肾脏健康表型医疗数据。目标2-确定肾脏健康变化的流行病学和临床结果。瞄准3- 开发和验证概率图形模型，以预测通过肾脏健康状态和确定进展的危险因素。拟议的研究很重要，因为我们将具有表型算法在多中心研究中验证的肾脏健康，可以增强其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，因为它在创新的步骤中实现数据科学和统计数据的技术进步，以开发和验证确定肾脏健康和图形变化的可计算表型的表型算法通过非线性和非正常时间通过肾脏健康状态预测过渡的模型使用高度颗粒状电子健康记录的依赖域。这将为改变的基础通过确定那些患有AKI的风险并发展到的患者的护理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以及肾脏健康的诊断和提示工具。