导航护士管理下的骨科外科护理:基于机器学习的风险预测模型,用于术后恢复质量和可解释的人工智能分析
Qiu Qian1, Jingrong Wu1, Zhi Xu2
1Department of Orthopaedic Surgery, Zhangjiagang Hospital Affiliated to Soochow University (Zhangjiagang First People's Hospital), Zhangjiagang, Jiangsu, China.
Medicine
|November 15, 2025
概括
导航护士管理 (NNM) 显著改善了骨科患者的康复质量. 机器学习模型,特别是随机森林,准确地预测恢复结果,帮助个性化护理.
科学领域:
- 整形外科手术 整形外科手术
- 护理管理 护理管理
- 医疗保健中的机器学习
背景情况:
- 优化术后恢复质量对于骨科患者至关重要.
- 导航护士管理 (NNM) 是一种提议的干预措施,以加强外科手术期间的护理.
- 预测模型可以识别影响恢复和指导干预的因素.
研究的目的:
- 评估NNM在骨科外科手术期间护理中的有效性.
- 开发和比较机器学习 (ML) 模型,以预测术后恢复质量.
- 确定影响部手术后恢复的关键因素.
主要方法:
- 对216名骨科部手术患者进行了回顾性研究.
- 应用后勤回归,名录和6个ML算法 (RF,XGBoost,SVM,DT,NB,LR) 的应用.
- 使用AUC,灵敏度,特异性,精度,F1分数的性能评估;通过SHAP分析解释性.
主要成果:
- NNM是改善恢复质量的独立预测指标.
- 随机森林 (RF) 获得了完美的预测得分 (AUC=1.000).
- 排放时的最终视觉模拟尺度是SHAP分析中发现的最有影响力的因素.
结论:
- 在骨科患者中,NNM显著提高了术后恢复质量.
- 与传统方法相比,ML模型,特别是RF,在预测回收方面提供了更高的准确性.
- SHAP分析有助于理解个性化患者护理的预测因素.
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