机器学习预测模型用于早期肠道营养患者的吸收风险,这些患者患有严重急性胰腺炎
Bo Zhang1, Huanqing Xu2, Qigui Xiao1
1Department of Hepatobiliary Surgery, The First Affiliated Hospital, Xi'an Jiaotong University, NO.277 Yanta West Road, Xi'an, China.
Heliyon
|December 10, 2024
概括
一个新的XGBoost模型准确地预测了接受早期肠道营养 (EN) 的严重急性胰腺炎患者的吸入风险. 这种工具有助于临床医生识别高风险个体,以改善患者的治疗结果.
科学领域:
- 关键护理医学 关键护理医学
- 胃肠病学 胃肠病学
- 医疗信息学 医疗信息学
背景情况:
- 严重急性胰腺炎 (SAP) 患者通常需要早期肠道营养 (EN).
- 吸血是严重病患者在EN期间面临的重大风险.
- 预测愿望风险对于及时干预至关重要.
研究的目的:
- 开发和验证一种风险预测模型,用于 SAP 患者接受早期 EN.
- 在这个患者群体中确定与渴望相关的关键风险因素.
主要方法:
- 对339名接受EN.SAP治疗的SAP患者进行了回顾性分析.
- 确定了志向的危险因素.
- 用6个机器学习算法构建了预测模型,并使用曲线下的面积 (AUROC) 进行验证.
主要成果:
- 在培训队列中的28.31%的患者中出现了吸血.
- 预测因素包括年龄,意识,机械呼吸,吸气史,营养风险和并发症.
- 与物流回归相比,XGBoost模型表现出更高的性能 (AUROC: 0.992,F1: 0.902),显示出更高的特异性和准确性.
结论:
- 该XGBoost模型有效地识别高风险的SAP患者的EN愿望.
- 该模型为临床应用提供了出色的分辨率和高精度.
- 开发的模型可以帮助临床医生在SAP患者中管理EN.
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