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作为癌症患者术后并发症的预测因素的体力活动趋势:一种机器学习方法

Carlos de Miguel Llorente¹, Sjoerd de Vries², Petra Bor¹

¹Department of Rehabilitation, Physiotherapy Science and Sport, University Medical Center Utrecht, Utrecht, The Netherlands.

|December 25, 2025

概括

仅仅是术后活动趋势无法可靠地预测瘤病患者的并发症. 未来的研究应该将活动数据与生理和实验室结果相结合,以改善术后问题的早期检测.

科学领域:

医疗信息学医疗信息学
医疗保健中的机器学习
手术瘤学手术瘤学

背景情况:

早期发现术后并发症对于改善患者的治疗结果至关重要.
目前的监测方法往往具有侵入性,并可能推迟必要的干预.
机器学习 (ML) 和人工智能 (AI) 提供了实时数据分析的潜力,就像加速计中的身体活动一样,作为早期预警信号.

研究的目的:

评估ML模型的预测能力,使用加速计衍生体力活动趋势来评估瘤病人的术后并发症.
为了比较不同ML分类器 (随机森林,XGBoost,物流回归) 和建模策略的性能.

主要方法:

来自外科瘤病房的常规护理数据分析 (2020年10月-2024年12月).
使用嵌套交叉验证评估三个ML分类器:随机森林 (RF),极端梯度提升 (XGB) 和后勤回归 (LR).
两种建模策略的比较:培训/测试没有低样本和培训与低样本,以平衡并发症和非并发症日.

主要成果:

射频的最佳表现是下样本系数为1 (AUC = 0.66).
后勤回归 (LR) 在没有低样本的情况下实现了最高的AUC (0.68).
XGBoost (XGB) 的表现始终较低 (AUC ≈ 0.63-0.64).

结论:

仅仅是术后活动趋势不足以预测大瘤手术后的并发症.
将加速度计数据与生理和实验室数据相结合,可以提高预测准确度.
进一步整合多式联络数据可以提高术后护理的临床价值.

关键词:

可穿戴设备可以穿戴.机器学习是机器学习.瘤学在瘤学方面.身体活动身体活动康复康复康复康复康复康复

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