决策树模型预测近视症在治疗的儿童:向精确的眼科
Jun-Wei Chen1, Chi-Jie Lu2,3,4, Chieh-Han Yu5
1Department of Medical Education, Linkou Chang Gung Memorial Hospital, Guishan Dist., Taoyuan City 333, Taiwan.
Diagnostics (Basel, Switzerland)
|August 28, 2025
概括
这项研究发现,基线球形等效 (SE) 是预测近视进展的关键因素. 更多的基线SE表示更高的风险,指导个性化的近视控制策略.
科学领域:
- 眼科 眼科
- 机器学习
- 儿童健康
背景情况:
- 短视是一个全球性的健康问题,
- 在儿童近视控制中使用局部氨酸,但治疗反应有显著的差异.
- 预测对阿特罗宾的个体反应对于有效的近视治疗至关重要.
研究的目的:
- 开发一种可解释的机器学习模型,用于预测儿童近视控制中对局部氨酸的个性化反应.
- 鉴定影响近视进展的关键危险因素.
- 支持近视管理的个性化临床决策.
主要方法:
- 在3年内对1545名儿童眼睛进行局部治疗的回顾性观察研究.
- 用于预测球形等效 (SE) 变化的分类和回归树 (CART) 模型.
- 与最小绝对收缩和选择运算符 (Lasso) 回归的CART模型性能比较.
主要成果:
- 卡特和拉索模型的预测性能相似.
- 基线球形等效 (SE) 被确定为近视进展的主要决定因素.
- 基线SE阴性超过3. 125 D的儿童表现出更大的进展,特别是在治疗时间更长和更高的阿特罗平剂量时.
结论:
- 基线SE是影响近视进展的关键因素.
- 可解释的机器学习,通过CART决策规则,可以提高近视的精确管理.
- 这项研究支持儿童近视控制的个性化治疗策略.
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