Developing Treatment Policies for Complex Patients Using Modeling and Data Mining

使用建模和数据挖掘为复杂患者制定治疗策略

• 批准号: 7670340
• 负责人: Paul E. Johnson
• 金额: $ 16.41万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-08 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7670340
• 关键词: Developing; Treatment; Policies; Complex; Patients

DESCRIPTION (provided by applicant): Patients with type 2 diabetes mellitus have high risk for cardiovascular events, and the risk derives from multiple sources including elevated glucose, blood pressure, lipids, and other factors. Prior studies have assessed cardiovascular risk in diabetes patients and several evidence-based clinical goals have been identified that independently reduce risks of future adverse cardiovascular events. Prior studies have estimated projected risks of future events for patients with type 2 diabetes, but these studies do not systematically evaluate strategies, risks, and treatment costs for diabetes patients. However, prior research has not provided usable information needed to compare the relative risks and benefits of multiple treatment policies that are available for diabetes care at successive points in time. Specifically, no research is available that estimates the relative impact on cardiovascular events or on costs of competing clinical policies that differentially emphasize glucose, BP, or lipid control, or the relative merits and drawbacks of a "feedforward" versus the more typical "feedback" clinical policy that typically characterizes care of complex patients. The research proposed here addresses these critical gaps in knowledge using modeling and data mining technologies to discover and structure clinical policies that most effectively reduce risk of cardiovascular events in complex patients with diabetes. The work will proceed in two steps: (a) Develop modeling methodology to identify physician treatment strategies (combinations of pharmaceutical agents, timing of clinical interventions, complexity of regimen, risky prescribing events) that minimize cost or risk of major cardiovascular complications in complex patients with diabetes, and (b) Apply computational modeling and data mining techniques to identify the optimal combinations of pharmaceutical agents to minimize pharmaceutical costs while achieving pre-specified degrees of reduction in risk of major cardiovascular complications in complex patients with diabetes. Specific objectives will examine the relative merits of clinical policies that prioritize different clinical domains, and the relative merits of "feedforward" versus "feedback" clinical strategies. Results will contribute to the important ongoing debate about comparative effectiveness of alternative clinical policies for complex patients with diabetes, including cost data needed to inform the development of clinical guidelines and public policy for the care of complex patients, whose needs are not well addressed by existing clinical guidelines. Moreover, the methods used in this project will provide a useful prototype for comparative effectiveness research that can be applied to diverse clinical domains and patient populations.

描述（由申请人提供）：2型糖尿病患者是发生心血管事件的高危患者，其风险来自多种因素，包括血糖升高、血压升高、血脂升高和其他因素。先前的研究已经评估了糖尿病患者的心血管风险，并且已经确定了几个基于证据的临床目标，可以独立地降低未来不良心血管事件的风险。先前的研究估计了2型糖尿病患者未来事件的预计风险，但这些研究没有系统地评估糖尿病患者的策略、风险和治疗成本。然而，先前的研究并没有提供可用的信息来比较在连续的时间点上可用于糖尿病护理的多种治疗政策的相对风险和收益。具体来说，目前还没有研究评估不同临床策略对心血管事件或成本的相对影响，这些临床策略不同地强调血糖、血压或血脂控制，或者“前馈”与更典型的“反馈”临床策略的相对优缺点，后者是复杂患者护理的典型特征。

项目成果

A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.

使用经过审查的，事件时间的数据，一种天真的贝叶斯机器学习方法来预测风险预测。

• DOI: 10.1002/sim.6526
• 发表时间: 2015-09-20
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Wolfson J;Bandyopadhyay S;Elidrisi M;Vazquez-Benitez G;Vock DM;Musgrove D;Adomavicius G;Johnson PE;O'Connor PJ
• 通讯作者: O'Connor PJ

Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting.

• DOI: 10.1016/j.jbi.2016.03.009
• 发表时间: 2016-06
• 期刊: Journal of biomedical informatics
• 影响因子: 4.5
• 作者: Vock DM;Wolfson J;Bandyopadhyay S;Adomavicius G;Johnson PE;Vazquez-Benitez G;O'Connor PJ
• 通讯作者: O'Connor PJ