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.

接收器操作特征（ROC）方法已经很好 作为解决决策的重要工具建立 医学和其他学科的不确定性。 INT评估如何 决策策略对回顾性二分法（双重）进行了分类， 或模糊（多估）事件，它为合理的依据 设计对潜在事件进行分类的决策策略。 患病率和错误成本因素很容易被纳入基于ROC的 决策设计。 该项目的目的是继续进行 ROC方法的研究与发展，适用于生物医学 研究，宣传ROC方法论的实际扩展 从这项研究和开发中，并提供计算服务， NIH壁内研究的ROC方法的支持和指导 社区。 在这些目的方面，我们进行了研究并进行了 使用ROC方法的实验，适用于现代生物医学 研究目标。 一个名为Roclab的用户友好，基于DOS的软件包已经 生产的计算ROC功能及其有用的派生功能的生产 用于离散和模糊的类成员资格数据。 决策策略 解释与患病率，错误分类有关的不确定性 成本和模糊的班级会员很容易由Roclab建造。 这些研究的一些结果已公开发表和介绍。 Roclab已安装在DCRT科学计算资源中 中心（SCRC）和程序包可供分发到 有兴趣的人。 ROC方法论仍然是重要的工具 生物医学研究。 增强ROC方法来支持生物医学 现代计算机上的研究和分发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方法与神经网络有着密切的关联 方法论，它正在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是有用的 为了响应用户反馈而开发新功能；应用 适当地提高了Roclab的新生物医学研究问题； 探索合并协变量以制定增强的决策规则；和 探索ROC方法与神经网络的结合 方法论。