一种基于机器学习的预测模型,用于经过腹腔镜胃肠道手术的高风险患者的延迟临床重要术后恶心和吐
Zhinan Zheng1, Yabin Huang1, Yingyin Zhao1
1Department of Anesthesia, The Sixth Affiliated Hospital, Sun Yat-sen University, No.26 Yuancunerheng Road, Guangzhou, 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, No.26 Yuancunerheng Road, Guangzhou, 510655, China.
American journal of surgery
|August 25, 2024
概括
机器学习准确地预测了手术后的延迟术后恶心和吐 (PONV). 这种可解释的模型识别了针对性预防策略的高风险患者.
科学领域:
- 医疗信息学 医疗信息学
- 手术瘤学手术瘤学
- 机器学习在医学中的应用
背景情况:
- 延迟的临床重要术后恶心和吐 (CIPONV) 可以导致显著的手术并发症.
- 准确预测CIPONV对于有效的患者管理至关重要.
研究的目的:
- 开发和验证基于机器学习的预测模型,用于延迟CIPONV.
- 为了利用腹腔镜胃肠道手术患者的术后数据.
主要方法:
- 分析了FDP-PONV试验中的1154名患者.
- 用LASSO进行特征选择,并对81个外科手术变量进行逐步回归.
- 一个随机森林模型因其卓越的预测性能而被选择.
主要成果:
- 确定了延迟CIPONV的六个关键预测因素.
- 随机森林模型在验证队列中实现了0.737的ROCAUC.
- 该模型的预测被解释使用夏普利的附加解释.
结论:
- 成功开发了一个可解释的机器学习模型来预测延迟的CIPONV.
- 该模型为患者提供了个性化风险评估.
- 帮助识别高风险个体进行主动预防策略.
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