RECEIVER OPERATING CHARACTERISTIC METHODOLOGY SUPPORT

接收器操作特性方法支持

• 批准号: 3774982
• 负责人: J M DELEO
• 金额: --
• 依托单位: CENTER FOR INFORMATION TECHNOLOGY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/3774982
• 关键词: computational neuroscience; computer data analysis; computer program /software; decision making; health science research analysis /evaluation; health science research support; mathematical model

Receiver Operating Characteristic (ROC) methodology has become well established as an important tool for addressing decision-making uncertainties in medicine and in other disciplines. Int evaluates how well a decision strategy classifies retrospective dichotomous (bivalent), or fuzzy (multivalued) events, and it provides a rational basis for designing decision strategies that classify prospective events. Prevalence and error cost factors are easily incorporated into ROC-based decision designs. The purpose of this project is to conduct continuing research and development in ROC methodology as applicable to biomedical research, to publicize practical extensions of ROC methodology derived from this research and development, and to provide computational service, support, and guidance in ROC methodology to the NIH intramural research community. Toward these ends we have conducted research and performed experimentation with ROC methodology as it applies to modern biomedical research objectives. A user-friendly, DOS-based software package called ROCLAB has been produced that computes ROC functions and their useful derived features for discrete and fuzzy class membership data. Decision strategies that account for uncertainties related to prevalence, false classification costs, and fuzzy class membership are easily constructed with ROCLAB. Some results of these studies have been published and presented publicly. ROCLAB has been installed in the DCRT Scientific Computing Resource Center (SCRC) and the program package is available for distribution to interested persons. ROC methodology remains an important tool in biomedical research. Enhancing ROC methodology to support biomedical research and distributing ROC computational tools on modern computer platforms are vital services to offer the biomedical research community. Furthermore, ROC methodology has close associations with neural network methodology, which is fast becoming an important computational tool in the biomedical sciences. Future plans include enhancing ROCLAB as useful new features are developed in response to user feedback; applying the enhanced ROCLAB to new biomedical research problems as appropriate; exploring combining covariates to produce enhanced decision rules; and exploring the use of ROC methods in conjunction with neural network methodology.

接收者操作特征 (ROC) 方法已变得成熟 被确立为解决决策问题的重要工具 医学和其他学科的不确定性。 Int 评估如何 决策策略将回顾性二分法（二价）分类， 或模糊（多值）事件，它为 设计对预期事件进行分类的决策策略。 流行率和错误成本因素很容易纳入基于 ROC 的 决策设计。 该项目的目的是持续进行 适用于生物医学的 ROC 方法的研究和开发 研究，宣传 ROC 方法的实际扩展 通过这项研究和开发，并提供计算服务， 对 NIH 校内研究的 ROC 方法的支持和指导 社区。 为了这些目的，我们进行了研究并进行了 ROC 方法的实验适用于现代生物医学 研究目标。 一个用户友好的、基于 DOS 的软件包，称为 ROCLAB 生成计算 ROC 函数及其有用的派生特征 用于离散和模糊类成员数据。 决策策略 考虑与患病率、错误分类相关的不确定性 使用 ROCLAB 可以轻松构建成本和模糊类成员资格。 这些研究的一些结果已经发表并公开展示。 ROCLAB 已安装在 DCRT 科学计算资源中 中心 (SCRC) 和程序包可分发给 有兴趣的人。 ROC 方法仍然是一个重要的工具 生物医学研究。 增强 ROC 方法以支持生物医学 在现代计算机上研究和分发 ROC 计算工具 平台是为生物医学研究界提供的重要服务。 此外，ROC方法与神经网络有密切的联系 方法论，正迅速成为重要的计算工具 生物医学科学。 未来的计划包括增强 ROCLAB 的实用性 根据用户反馈开发新功能；应用 适当增强 ROCLAB 以应对新的生物医学研究问题； 探索组合协变量以产生增强的决策规则；和 探索 ROC 方法与神经网络的结合使用 方法论。