RECEIVER OPERATING CHARACTERISTIC METHODOLOGY SUPPORT

接收器操作特征(ROC)方法已变得很好 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评估方式 好的，决策策略分类为回溯二分(二价)， 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被合并到基于ROC的 决策设计。该项目的目的是继续进行 适用于生物医学的ROC方法学的研究与开发 研究，宣传ROC方法论的实践扩展 通过这项研究和开发，并提供计算服务， 中华民国方法论对美国国立卫生研究院内部研究的支持和指导 社区。为此，我们进行了研究，并进行了 ROC方法论在现代生物医学中的应用 研究目标。 一个用户友好的、基于DOS的软件包ROCLAB已经 生成的用于计算ROC函数及其有用派生特征的 用于离散和模糊类成员资格数据。决策策略 考虑到与流行率、错误分类相关的不确定性 使用ROCLAB可以很容易地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)，程序包可分发至 有利害关系的人。ROC方法论仍然是 生物医学研究。加强ROC方法论支持生物医学 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社区提供的重要服务。 此外，ROC方法论与神经网络有着密切的联系 方法学，它正在迅速成为一个重要的计算工具 生物医学科学。未来计划包括加强ROCLAB作为有用的 根据用户反馈开发了新功能；应用 适当地加强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；以及 探索ROC方法与神经网络的结合使用 方法论。

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.