Real time risk prognostication via scalable hazard trees and forests

通过可扩展的危险树和森林进行实时风险预测

• 批准号: 10655749
• 负责人: Hemant Ishwaran
• 金额: $ 55.74万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10655749
• 关键词: Affect; American; Benchmarking; Cessation of life; Code; Computer software; Data; Devices; Dialysis procedure; Disease; Electronic Health Record; Ensure; Event; Goals; Hazard Models; Heart; Heart Transplantation; Heart failure; Individual; Information Technology; Kidney Failure; Learning; Libraries; Machine Learning; Measurement; Methodology; Methods; Modeling; Modernization; Multicenter Studies; Organ failure; Patients; Performance; Procedures; Prognosis; Risk; Risk Assessment; Survival Analysis; System; Time; Trees; Update; Writing; adverse outcome; comorbidity; cost; data complexity; demographics; flexibility; forest; hazard; health data; hemodynamics; implantation; improved; innovation; left ventricular assist device; machine learning method; mechanical circulatory support; mortality; novel strategies; open source; personalized risk prediction; platform-independent; population health; portability; predictive modeling; prognostication; risk prediction; risk prediction model; statistics; success; tool; user friendly software; user-friendly; web page

Project Summary/Abstract Wearable sensing devices and Electronic Health Records (EHRs) are some examples of emerging information technologies expected to generate huge volumes of data recording individual’s health data over time. If properly utilized, these data provide a treasure trove of information for building real-time warning systems for adverse outcomes and to construct individualized risk prediction. To model the dynamic changes of covariate effects, time-varying survival models have emerged as a powerful approach. To deal with the size and complexity of data, with potential interactions among large number of variables, and interactions with time itself, we propose a state of the art machine learning approach using hazard trees and forests for estimating flexible hazard models with time-dependent covariates. Scalable and user friendly open source software implementing the methodology will be developed and made publicly available. The software will be applied to a rich, multicenter study of heart failure patients listed for heart transplantation to develop a state of the heart hazard risk prediction model.

项目概要/摘要 可穿戴传感设备和电子健康记录 (EHR) 是新兴技术的一些例子 信息技术预计将产生大量记录个人健康数据的数据 如果利用得当，这些数据将为构建实时预警提供信息宝库。 不良结果的系统并构建个性化风险预测对动态进行建模。 时变生存模型已成为应对协变量效应变化的有效方法。 数据的大小和复杂性，大量变量之间的潜在相互作用以及相互作用 随着时间的推移，我们提出了一种最先进的机器学习方法，使用危险树和森林来进行 估计具有时间相关协变量的灵活危险模型可扩展且用户友好的开源。 将开发并公开实施该方法的软件。 应用于一项内容丰富的多中心研究，对象是接受心脏移植的心力衰竭患者，以形成一种状态 心脏危险风险预测模型。