Quantitative Modeling Software with Applications to Medical Decision Making

定量建模软件在医疗决策中的应用

• 批准号: 10823037
• 负责人: Smita Nayak
• 金额: $ 42万
• 依托单位: BERKELEY MADONNA, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10823037
• 关键词: Acceleration; Achievement; Adjusted Life Years; Adverse event; Caring; Characteristics; Clinical Trials; Code; Cohort Analysis; Communities; Complex; Computer Models; Computer software; Cost Effectiveness Analysis; Coupled; Decision Analysis; Decision Making; Decision Modeling; Decision Trees; Development; Differential Equation; Disease; Educational process of instructing; Effectiveness; Elements; Event; Evidence Based Medicine; Evidence based practice; Explosion; Face; Functional disorder; Goals; Graph; Growth; Health; Health Care Costs; Healthcare; Healthcare Systems; Human; Image; Individual; Industry; Intervention; Intuition; Knowledge; Letters; Link; Mathematics; Measures; Medical; Medical Research; Methods; Modeling; Outcome; Output; Patients; Performance; Phase; Physicians; Physiology; Probability; Publishing; Pythons; Quality-Adjusted Life Years; Reporting; Research; Research Personnel; Running; Small Business Innovation Research Grant; Speed; System; Technology; Testing; Time; Trees; Uncertainty; Work; analytical method; burden of illness; cohort; computerized tools; cost; design; experience; flexibility; graphical user interface; health assessment; incremental cost-effectiveness; individual patient; interest; mathematical model; model building; multithreading; parallelization; portability; programs; recruit; relative cost; simulation; simulation software; stem; success; syntax; tool; treatment choice; treatment strategy; user-friendly

Project Summary/Abstract In recent years, health care systems and physicians have made concerted efforts to practice evidence-based medicine and provide patients with the best available information when making choices about their medical care. However, medical decisions are often complex with many uncertainties and potential outcomes to consider, some beneficial and some adverse. A popular analytic method used to help identify best treatment strategies while accounting for uncertainty is decision analysis, which typically involves computer modeling of a treatment choice outlined in the form of a decision tree, which shows options and health outcomes that may occur as a result of the choice made. Complex decision trees are evaluated via Monte Carlo microsimulation to allow for variability in individual patient characteristics and trace a patient’s path through the tree; when the microsimulation is repeated many times to simulate many individuals, it provides the probability of each potential outcome resulting from the initial decision. From this probability distribution, quantitative measures associated with each decision can be calculated such as life years, quality-adjusted life years (a generic measure of disease burden), and others; furthermore, when costs are also incorporated, cost-effectiveness analysis (CEA) can be performed to compute the incremental cost-effectiveness of each option. In this proposal, we describe plans to add functionality to the mathematical modeling software Berkeley Madonna to allow users to build decision trees and carry out Monte Carlo microsimulations and Markov cohort analysis. Berkeley Madonna’s interface was designed to make mathematical modeling quick and easy for non-technical users by using a simple syntax and graphical images to construct sophisticated differential equations. We will leverage this easy-to-use interface to enable medical researchers to perform microsimulation with software that is more user-friendly, transparent, powerful, and affordable than currently available options. In Aim 1, we propose further development of our decision analysis user interface that allows users to graphically construct decision trees and perform microsimulations. In this aim, in addition to optimizing tools and features for the GUI, we will add CEA output reports and graphics, sensitivity analysis capabilities, and Markov cohort analysis capabilities. We will create tutorials and a user guide as well as ready-made templates that provide users a jumping off point for quickly making their own models. In Aim 2, we propose to optimize code for performance on single CPUs, multiple CPUs, and GPUs. Analysis speed is important because large, complex models can take weeks to months to run with currently available software, none of which harness the power of GPU technology; successful completion of this aim would make Berkeley Madonna the fastest available software by far for performing decision analysis microsimulations. Finally, we will carry out extensive beta testing. Achievement of these goals will provide an easy-to-use, transparent, powerful, and affordable tool to biomedical researchers, educators, and professionals, and positively impact scientific discovery.

项目概要/摘要 近年来，医疗保健系统和医生共同努力实践循证医学 并在患者做出医疗选择时为患者提供最佳可用信息 然而，医疗决策往往很复杂，存在许多不确定性和潜在的结果。 考虑一些有益的和一些不利的一种流行的分析方法，用于帮助确定最佳治疗方法。 考虑不确定性的策略是决策分析，通常涉及对 以决策树形式概述的治疗选择，显示了可能的选择和健康结果 通过蒙特卡罗微观模拟评估复杂的决策树以进行选择。 考虑到个体患者特征的变化，并在发生变化时通过树追踪患者的路径； 微观模拟被重复多次以模拟许多个体，它提供了每个个体的概率 从这个概率分布中，可以得出初始决策的潜在结果。 可以计算与每个决策相关的生命年、质量调整生命年（一个通用的 此外，当将成本也考虑在内时，成本效益 可以执行分析（CEA）来计算每个选项的增量成本效益。 提案中，我们描述了为数学建模软件 Berkeley Madonna 添加功能的计划 允许用户构建决策树并进行蒙特卡罗微观模拟和马尔可夫队列分析。 Berkeley Madonna 的界面旨在让非技术人员快速轻松地进行数学建模 用户通过使用简单的语法和图形图像来构造复杂的微分方程。 利用这个易于使用的界面，医学研究人员能够使用以下软件执行微观模拟： 在目标 1 中，我们比当前可用的选项更加用户友好、透明、功能强大且价格实惠。 进一步开发我们的决策分析用户界面，允许用户以图形方式构建 为了实现这一目标，除了优化工具和功能之外，还可以构建决策树并执行微观模拟。 GUI，我们将添加 CEA 输出报告和图形、敏感性分析功能和马尔可夫队列分析 我们将创建教程和用户指南以及现成的模板，为用户提供 快速制作自己的模型的起点 在目标 2 中，我们建议优化代码以提高性能。 在单 CPU、多 CPU 和 GPU 上，分析速度很重要，因为大型、复杂的模型可能会影响分析速度。 使用当前可用的软件需要几周到几个月的时间才能运行，这些软件都没有利用 GPU 的能力 技术；成功完成这一目标将使 Berkeley Madonna 成为最快的可用软件 最后，我们将进行广泛的 Beta 测试。 这些目标的实现将为人们提供一个易于使用、透明、强大且负担得起的工具 生物医学研究人员、教育工作者和专业人士，并对科学发现产生积极影响。