简单的Nomo:一个Python包,用于制作Nomograms,用于可视化计算物流回归模型
Haoyang Hong1,2, Shenda Hong1
1National Institute of Health Data Science, Peking University, Beijing, China.
Health data science
|March 15, 2024
概括
simpleNomo是一个新的Python工具箱,可以从逻辑回归系数创建名ograms,使临床预测更容易获得. 该工具有助于将研究转化为患者护理,特别是在资源有限的环境中.
科学领域:
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
- 临床预测建模临床预测建模
背景情况:
- 后勤回归模型对于临床预测至关重要,但在资源乏或离线环境中面临挑战.
- 现有的名ogram生成器往往需要原始数据,限制使用只有系数的既定模型.
- 需要工具直接从逻辑回归系数生成名ograms,以提高可用性和临床翻译.
研究的目的:
- 开发一个开源的Python工具箱,简单Nomo,用于从逻辑回归模型构建名ograms.
- 仅使用模型系数来实现名录生成,克服现有工具的局限性.
- 创建一个相关的在线网站,用于名ogram生成.
主要方法:
- 设计和开发了开源Python工具箱simpleNomo.
- 实现了功能,可以直接从逻辑回归系数生成名ograms.
- 开发了一个配套的在线网站,用于创建名ogram.
主要成果:
- 简单Nomo成功生成了保持原始物流回归模型预测精度的nomograms.
- 该工具是用户友好和易于遵循的.
- simpleNomo与Python 3兼容,可以通过PyPI和GitHub轻松获得.
结论:
- simpleNomo提供了一个有价值的解决方案,用于从逻辑回归系数创建名ograms.
- 该工具箱提高了临床预测模型的可访问性和实际应用,特别是在不同的环境中.
- simpleNomo 便于将现有的逻辑回归模型转换为更广泛使用的实用名ograms.
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