基于机器学习的模型用于手术期间的血液流失,用于逆腹膜腹腔镜上腺切除术
Jing He1, Jiahui Zhao2, Yong Luo3
1Department of Undergraduate College, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing, 100029, China.
Surgical endoscopy
|January 27, 2026
概括
机器学习模型可以预测腹膜腹腔镜上腺切除术 (RLA) 中的手术内失血 (IBL). 关键因素包括性别,外科医生经验和病变特征,有助于进行外科风险评估.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 手术瘤学手术瘤学
- 数据科学数据科学数据科学
背景情况:
- 在手术期间流失的血液 (IBL) 是一个显著的风险因素,并发症期间和之后的逆腹腔镜上腺切除术 (RLA).
- 准确预测IBL对于患者安全和手术规划至关重要.
研究的目的:
- 在RLA患者中确定与IBL相关的风险因素.
- 开发和比较机器学习 (ML) 模型,用于预测IBL.
主要方法:
- 对530个RLA病例 (2014-2021) 的回顾性分析.
- 使用LASSO回归的特征选择.
- 开发和评估ML模型,包括后勤回归 (LR),随机森林 (RF) 和神经网络 (NN).
主要成果:
- 性别,外科医生经验,疾病类型,病变直径和位置被确定为高IBL (≥100毫升) 的预测因素.
- LR,RF和NN模型表现出强大的预测性能,AUC分别为0.775,0.769和0.729.
- 开发了一种临床名录和在线计算器,显示出出色的临床实用性.
结论:
- ML模型有效地预测RLA中的IBL,协助手术风险评估.
- 这些模型为量身定制的治疗策略提供了一个非侵入性的工具.
- 该研究强调了ML在外科手术的临床实践中的潜力.
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