基于医生手术选择数据的机器学习辅助的近视折射手术类型的选择
Jiajing Li1,2, Yuanyuan Dai3, Zhicheng Mu3
1School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, China. lijiajing@aliyun.com.
BMC medical informatics and decision making
|February 8, 2024
概括
机器学习模型现在可以帮助眼科医生选择最好的近视角膜折射手术. 一个随机森林模型实现了高精度,模仿专家的决策,以改善患者的治疗结果.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 角膜折射手术是一种常见的视力校正手术.
- 选择最佳手术需要考虑患者的具体情况和外科医生的经验.
- 现有的方法可能无法完全捕捉眼科医生决策的细微差别.
研究的目的:
- 开发和评估用于协助角膜折射手术选择的机器学习模型.
- 用临床数据复制眼科医生决策过程.
- 为了提高选择近视折射手术的准确性和效率.
主要方法:
- 利用了7,081名接受角膜折射手术的患者 (2000-2017年) 的临床数据.
- 数据预处理包括清理和处理缺失的值,随后是SMOTE用于类不平衡.
- 6个机器学习模型 (NBM,RF,AdaBoost,XGBoost,BP,DBN) 通过使用10倍交叉验证和网格搜索进行训练和优化.
主要成果:
- 随机森林 (RF) 模型表现出卓越的性能,与眼科医生决定达成0.8775的一致性.
- 射频模型的宏观F1得分达到了0.8019,表明了强大的分类.
- SHAP分析证实,该模型的特征重要性与临床专业知识一致.
结论:
- 机器学习,特别是射频模型,可以有效地帮助眼科医生选择合适的角膜折射手术.
- 开发的模型显示了与专家决定的高度一致,并提供了可解释的见解.
- 这种方法有可能在折射手术规划中产生显著的有益临床效应.
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