RECEIVER OPERATING CHARACTERISTIC METHODOLOGY SUPPORT

受试者工作特征（ROC）方法已经变得很好 作为处理决策问题的一个重要工具 医学和其他学科的不确定性。 Int评估如何 那么决策策略将回顾性二分（二价）分类， 或模糊（多值）事件，它提供了一个合理的基础， 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 该项目的目的是进行持续的 研究和开发适用于生物医学的ROC方法 研究，宣传ROC方法的实际扩展， 从这个研究和发展，并提供计算服务， 支持和指导ROC方法学对NIH内部研究 社区 为了实现这些目标，我们进行了研究， 实验与ROC方法，因为它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， 计算ROC函数及其有用的衍生特征的 对于离散和模糊的类成员关系数据。 决策策略， 考虑到与流行率、错误分类 成本和模糊类成员很容易与ROCLAB构建。 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）和程序包可分发到 感兴趣的人。 ROC方法仍然是一个重要的工具， 生物医学研究 改进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方法与神经网络有着密切的联系 方法，这是迅速成为一个重要的计算工具， 生物医学科学 未来的计划包括加强ROCLAB， 根据用户反馈开发了新功能;应用 加强ROCLAB，以适应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.