基于机器学习的预测模型,用于ICU患者的肠道营养相关腹及其护理应用
Xiaoying Liao1, Chunhua Li1, Qunyan Liu1
1Shangrao People's Hospital, Shangrao, China.
Frontiers in nutrition
|July 10, 2025
概括
机器学习模型可以预测ICU患者的肠道营养相关腹 (ENAD) 风险. 这种工具有助于早期干预,改善接受肠道营养的重症患者的护理.
科学领域:
- 关键护理医学 关键护理医学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 肠道营养相关腹 (ENAD) 是重症患者的常见并发症.
- 早期预测ENAD风险对于及时干预和更好的患者结果至关重要.
- 准确的风险分层对于在重症监护室管理ENAD至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测ICU患者的ENAD风险.
- 评估基于ML的ENAD预测模型在护理实践中的适用性.
- 增强患者对肠道营养的临床决策.
主要方法:
- 对接受肠道营养的ICU患者数据的回顾性分析.
- 使用LASSO回归来选择特征,并评估了9个ML算法.
- 为了模型的解释性和特征的重要性,采用了夏普利添加式解释 (SHAP).
主要成果:
- 随机森林 (RF) 模型显示了最高的区分能力,AUC为0.777.
- 一个使用12个预测因子的简化射频模型在减小维度后获得了0.754的AUC.
- 功能重要性分析确定了ENAD风险的关键预测因素.
结论:
- 开发的射频模型提供了一个可靠和可解释的工具,用于在ICU设置中预测ENAD风险.
- 这种ML模型可以支持有针对性的护理干预,并改善患者护理.
- 机器学习显示出个性化护理和增强临床决策的巨大潜力.
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