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通过机器学习预测24小时眼内压力峰值和平均值

Ranran Chen¹, Jinming Lei², Yujie Liao¹

¹Department of Ophthalmology, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.

Frontiers in medicine

|October 22, 2024

概括

机器学习使用特定时间点准确预测24小时眼内压力 (IOP). 这通过提供高峰值和平均值的精确IOP预测来增强青光眼的管理.

科学领域:

眼科医生眼科眼科
医疗信息学医疗信息学
人工智能的人工智能

背景情况:

准确的24小时眼内压力 (IOP) 监测对于诊断和治疗青光眼至关重要.
目前的方法可能无法完全捕捉白天IOP变化,影响治疗疗效.

研究的目的:

开发和验证用于预测24小时峰值和平均IOP的机器学习 (ML) 模型.
确定最佳时间点和ML算法,以提高预测准确度.
发现影响IOP预测的关键特征,以获得更好的临床见解.

主要方法:

电子医疗记录的回顾性分析 (2014年1月 - 2024年5月),包括517名患者的24小时IOP数据.
使用五个ML算法进行预测模型的培训和评估.
通过测试五个特定时间点 (上午10点,下午12点,下午2点,下午4点,下午6点) 的组合来优化预测准确度.
使用Shapley添加式解释 (SHAP) 进行特征重要性评估.

主要成果:

随机森林回归 (RFR) 模型证明了峰值和平均IOP预测的最佳性能.
对于24小时的峰值IOP,使用上午10点,下午12点,下午2点和下午4点的RFR数据,实现了R2=0.823.
对于24小时平均IOP,使用上午10点,下午12点,下午4点和下午6点的RFR数据,实现了R2=0.918.

结论:

开发的ML模型,特别是RFR,有效地预测24小时的峰值和平均IOP.
确定了特定的时间点组合,可以显著提高预测准确度.
这些发现为更有效的青光眼管理和个性化治疗策略提供了潜力.

关键词:

24小时 24小时 24小时玻璃眼 glaucoma 玻璃眼玻璃眼玻璃眼玻璃眼眼内压力眼内压力机器学习是机器学习.测量过程中的测量.它们是夜间的夜间动物.

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