个性化眼科麻醉:对患者特征,手术概况和麻醉方案进行回归分析,以预测结果
Iram Shahzadi1, Summar Fatima2, Samreen Ameen3
1Anesthesia, Combined Military Hospital (CMH)Sheikh Khalifa Bin Zayed Al Nahyan Hospital (SKBZ) Muzaffarabad, Muzaffarabad, PAK.
Cureus
|July 10, 2025
概括
使用机器学习开发了个性化眼科麻醉的预测模型. 随机森林模型准确地预测了患者的康复时间,识别了手术流血和BMI等关键因素.
科学领域:
- 麻醉学 麻醉学
- 数据科学数据科学数据科学
- 眼科医生 眼科 眼科
背景情况:
- 个性化麻醉方案对于优化眼科手术患者的治疗结果至关重要.
- 预测建模可以根据个体患者的特征增强对麻醉的量身定制.
研究的目的:
- 开发和验证一种机器学习模型,用于预测眼科麻醉中恢复时间和术后满意度.
- 确定影响康复时间的关键临床特征.
主要方法:
- 使用了350个模拟患者记录和75个临床特征的合成数据集.
- 应用探索性数据分析,推断统计和机器学习技术,包括随机森林.
- 采用递归特征消除 (RFE) 和SHAP分析来进行特征选择和解释.
主要成果:
- 随机森林模型实现了很高的预测准确性 (R2=0.91,MAE=0.11,RMSE=0.14).
- 恢复时间的关键预测因素包括手术输血,体重指数 (BMI) 和血小板计数.
- 恢复时间的非正常性得到证实,支持使用非参数方法.
结论:
- 机器学习模型,特别是随机森林,显示了个性化眼科麻醉协议的巨大潜力.
- 该研究强调了患者人口统计学和手术因素在预测康复中的重要性.
- 建议在未来的研究中使用现实世界的临床数据进行外部验证.
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