机器学习算法来预测外围注射器相关的孕产妇发烧:一项回顾性研究
Xiaohui Guo1,2,3, Haixia Zhang1,3,4, Hongliang Mei1,3,4
1Department of Pharmacy, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China.
Frontiers in pharmacology
|June 26, 2025
概括
预测与外周管相关的孕产妇发烧 (ERMF) 是一个挑战. 一个后勤回归模型有效地确定了八个关键的风险因素,使得接受外周止痛治疗的孕妇患者能够更好地做出临床决策.
科学领域:
- 产科和妇科 产科和妇科
- 临床信息学 临床信息学
- 流行病学 流行病学
背景情况:
- 脊柱外相关的孕产妇发烧 (ERMF) 是患者控制的脊柱外止痛 (PCEA) 的不可预测并发症.
- 准确预测ERMF对于个性化产科护理和及时干预至关重要.
研究的目的:
- 开发和验证ERMF的预测模型,使用现实世界的数据.
- 确定ERMF有助于临床决策的重要贡献因素.
主要方法:
- 在2021年10月至2023年3月期间接受PCEA的1492名妇女的回顾性分析.
- 开发和比较六个机器学习模型,包括后勤回归 (LR) 和支持矢量机器 (SVM).
- 使用曲线下的面积 (AUC),校准曲线和决策曲线分析评估模型性能.
主要成果:
- 24.3%的妇女 (362个案例) 经历了欧洲货币基金基金.
- 与SVM模型相比,LR模型显示出更高的校准性 (布赖尔分数:0.193).
- 确定了ERMF的八个显著预测因素:中性粒细胞百分比,分娩阶段1,羊水污染,人造膜破裂,膜炎,以及特定的催产素/抗微生物/dinoprostone使用.
结论:
- 后勤回归模型为预测ERMF风险提供了一种实用和有效的方法.
- 识别关键预测因素可以实现更有针对性的临床管理,并可能减少ERMF发生率.
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