聊天GPT增强的ROC分析 (CERA):一个闪亮的网络工具,用于在生物标志物分析中找到最佳切断点.
Melih Agraz1,2, Christos Mantzoros3, George Em Karniadakis2,4
1Department of Statistics, Giresun University, Giresun, Turkiye.
PloS one
|April 10, 2024
概括
本研究介绍了CERA,这是一个用户友好的网络工具,用于接受器运行特性 (ROC) 曲线分析. 通过整合ChatGPT来进行输出解释,CERA简化了诊断测试性能评估,使复杂的分析更容易获得.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 诊断测试对于疾病的识别至关重要.
- 接收器操作特征 (ROC) 曲线分析是评估诊断测试性能的一个关键方法.
- 在ROC分析中确定最佳切断点至关重要,但往往复杂.
研究的目的:
- 开发一个易于使用的ROC曲线分析工具.
- 为了简化确定诊断测试最佳切断点的过程.
- 为了提高ROC分析结果的可访问性和理解性,对于没有编码专业知识的用户.
主要方法:
- 开发了一个名为CERA (ChatGPT增强的ROC分析) 的网络工具,使用Shiny接口.
- 整合ChatGPT用于解释ROC分析输出.
- 使用R-Markdown.生成解释的报告.
主要成果:
- CERA提供了一个用户友好的界面,用于更快,更有效的ROC曲线分析.
- 该工具简化了数据预处理和分析,减少了对编码技能的需求.
- 聊天GPT集成增强了分析结果的解释和理解.
结论:
- 在ROC曲线分析的可访问性方面,CERA提供了显著的改进.
- 该工具使研究人员和临床医生没有广泛的编码经验,可以执行和解释诊断测试评估.
- 基于诊断测试性能指标,CERA促进了更快,更知情的决策.
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