权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Efficient Statistical Learning Methods for Personalized Medicine Using Large Scale Biomedical Data

使用大规模生物医学数据进行个性化医疗的高效统计学习方法

基本信息

批准号：
9891071
负责人：
Yuanjia Wang
金额：
$ 32.89万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9891071
关键词：
Academic Medical Centers Address Adverse event Algorithms Benefits and Risks Categories Center for Translational Science Activities Classification Clinical Collaborations Collection Complement Complex Computer software Data Data Collection Databases Decision Making Dependence Dimensions Documentation Electronic Health Record Ensure Equilibrium Exclusion Criteria Formulation Gaussian model Goals Health Health Status Healthcare Healthcare Systems Heterogeneity Indiana Informatics International Knowledge Laboratories Learning Length Measurement Measures Medical Methodology Methods Modeling Nature Non-Insulin-Dependent Diabetes Mellitus Outcome Patient Care Patient-Focused Outcomes Patients Pattern Pharmaceutical Preparations Process Quality Control Randomized Controlled Trials Records Research Risk Safety Sampling Structure Testing Time Treatment Protocols adaptive learning adverse event risk algorithmic methodologies analytical tool base big biomedical data clinical data warehouse clinical decision-making clinical encounter clinical practice comorbidity complex data data modeling data space design evidence base feature extraction heterogenous data individual patient individualized medicine knowledge base learning strategy machine learning method novel optimal treatments outreach patient population personalized decision personalized medicine population based scale up statistical learning temporal measurement theories treatment effect treatment guidelines treatment response treatment strategy

项目摘要

Project Summary/Abstract Current medical treatment guidelines largely rely on data from randomized controlled trials that study average effects, which may be inadequate for making individualized decisions for real-world patients. Large-scale electronic health records (EHRs) data provide unprecedented opportunities to optimize personalized treatment strategies and generate evidence relevant to real-world patients. However, there are inherent challenges in the use of EHRs, including non-experimental nature of data collection processes, heterogeneous data types with complex dependencies, irregular measurement patterns, multiple dynamic treatment sequences, and the need to balance risk and benefit of treatments. Using two high-quality EHR databases, Columbia University Medical Center's clinical data warehouse and the Indiana Network for Patient Care database, and focusing on type 2 diabetes (T2D), this proposal will develop novel and scalable statistical learning approaches that overcome these challenges to discover optimal personalized treatment strategies for T2D from real-world patients. Specifically, under Aim 1, we will develop a unified framework to learn latent temporal processes for feature extraction and dynamic patient records representation. Our approach will accommodate large-scale variables of mixed types (continuous, binary, counts) measured at irregular intervals. They extract lower-dimensional components to reflect patients' dynamic health status, account for informative healthcare documentation processes, and characterize similarities between patients. Under Aim 2, we will develop fast and efficient multi-category machine learning methods, in order to evaluate treatment propensities and adaptively learn optimal dynamic treatment regimens (DTRs) among the extensive number of treatment options observed in the EHRs. The methods will provide sequential decisions that determine the best treatment sequence for a T2D patient given his/her EHRs. Under Aim 3, we will develop statistical learning methods to assist multi-faceted treatment decision-making, which balances risks versus benefits when evaluating a DTR. Our approach will ensure maximizing benefit to the greatest extent while controlling all risk outcomes under the safety margins. For all aims, we will develop efficient stochastic resampling algorithms to scale up the optimization for massive data sizes. We will identify optimal DTRs for T2D using the extracted information from patients' comorbidity conditions, medications, and laboratory tests, as well as records-collection processes. Our methodologies will be applied and cross-validated between the two EHR databases. The treatment strategies learned from the representative EHR databases with a diverse patient population will be beneficial for individual patient care, assisting clinicians to adaptively choose the optimal treatment for a patient. Finally, we will disseminate our methods and results through freely available software and outreach to the informatics and clinical experts at our Centers for Translational Science and elsewhere.

项目总结/文摘