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机器学习的应用,以预测术后恶心和吐在拉皮镜袖子胃切除术中的情况

Xiaodong Shan^1,2, Mingchuang Zhang³, Ying Liang⁴

¹Department of Pancreatic and Metabolic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.

Obesity surgery

|June 3, 2025

概括

机器学习准确地预测了腹腔镜袖子胃切除术 (LSG) 后的术后恶心和吐 (PONV). XGBoost模型确定了关键的风险因素,使得更好的患者护理和PONV.风险管理成为可能.

科学领域:

医疗信息学医疗信息学
手术瘤学手术瘤学
医疗保健中的机器学习

背景情况:

手术后恶心和吐 (PONV) 是腹腔镜袖子胃切除术 (LSG) 后的常见并发症.
预测PONV风险对于优化LSG后患者管理至关重要.

研究的目的:

开发和验证用于预测LSG患者PONV风险的机器学习模型.
在LSG群体中确定PONV的独立预测因子.

主要方法:

利用了860名LSG患者的数据 (2018年1月至2023年3月).
采用Boruta算法和逻辑回归来进行因子识别.
开发和比较多个机器学习模型,包括XGBoost,随机森林和SVM.
使用接收器操作特征曲线 (AUROC) 下面的面积来评估模型性能.

主要成果:

55%的患者经历了PONV.
确定的风险因素:女性性别,手术持续时间>60分钟,手术内雷米芬坦尼尔和术后阿片类药物.
预防性抗药显示有保护作用.
该XGBoost模型实现了0.828.8的最高AUROC.
创建了一个在线PONV预测工具.

结论:

该XGBoost模型提供高准确性预测在LSG患者的PONV.
建议进行外部验证,以确认模型的通用性.
开发的工具可以帮助临床决策对PONV预防.

关键词:

腹和代谢手术的治疗方法机器学习是机器学习.手术后的恶心和吐.预测模型是一个预测模型.

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