可解释的机器学习模型可以预测重灌综合征在严重急性胰腺炎患者
Cui Wu1, Shuangshuang Jing2,3, Dinghui Guo2
1Department of Nursing, Xuzhou Tongshan District Hospital of Traditional Chinese Medicine, Xuzhou, Jiangsu, China.
Frontiers in nutrition
|February 9, 2026
概括
这项研究开发了一种渐变增强机模型,用于预测重灌综合征 (RFS) 在严重急性胰腺炎 (SAP) 患者中的风险. 该模型识别了高风险个体,以便及时进行肠道营养干预.
科学领域:
- 医学科学 医学科学 医学科学
- 临床研究 临床研究
- 医疗信息学 医疗信息学
背景情况:
- 再养综合征 (RFS) 对患有严重急性胰腺炎 (SAP) 的患者构成重大风险.
- 早期识别患有RFS风险的患者对于及时干预和改善结果至关重要.
- 目前在SAP患者中进行RFS风险评估的方法需要提高精度和及时性.
研究的目的:
- 在重症急性胰腺炎 (SAP) 患者中构建和验证重新养综合征 (RFS) 的预测风险模型.
- 在明显的电解质异常出现之前,识别易患RFS的高风险个体.
- 为优化肠道营养启动时间和个性化干预提供决策支持.
主要方法:
- 一个回顾性队列研究,涉及SAP患者从2018年9月到2025年9月.
- 使用最小绝对收缩和选择运算符 (LASSO) 回归来进行特征选择,以及六个机器学习 (ML) 算法来进行模型开发.
- 使用接收机运行特征 (ROC) 曲线,校准曲线,决策曲线和夏普利添加式扩展 (SHAP) 分析评估模型性能.
主要成果:
- 确定了七个预测特征,血清,血清和血清是关键指标.
- 梯度增强机 (GBM) 模型表现出强大的预测性能 (AUC训练:0.851,AUC测试:0.762).
- SHAP分析证实了特征的重要性,包括胃肠道减压,BUN,DM史和利尿剂使用.
结论:
- 开发的GBM模型有效地预测了SAP患者的再养综合征风险.
- 这个工具可以帮助临床医生做出有关肠道营养启动的明智决定.
- 基于预测的RFS风险的个性化干预可以改善患者的管理和结果.
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