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逻辑回归和人工神经网络模型的比较,用于预测非ICU糖尿病患者的低血糖症
Fei Shao1, Guifang Lin1, Feihui Zeng2
1Department of Endocrinology and Metabolism Nursing, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, 350001, China.
BMC endocrine disorders
|December 11, 2025
概括
这项研究开发了一种后勤回归 (LR) 模型,用于使用电子健康记录预测糖尿病住院患者的低血糖风险. 在临床应用中,LR模型的价值与人工神经网络 (ANN) 相当.
科学领域:
- 临床信息学 临床信息学
- 糖尿病管理 糖尿病管理
- 预测分析是一种预测分析.
背景情况:
- 低血糖是糖尿病住院患者的重大风险.
- 预测低血糖症对于患者的安全和有效的糖尿病护理至关重要.
- 电子健康记录 (EHR) 为预测建模提供了丰富的数据来源.
研究的目的:
- 开发和验证非重症监护室 (非ICU) 糖尿病住院患者低血糖风险的预测模型.
- 为了比较物流回归 (LR) 和人工神经网络 (ANN) 模型在预测低血糖方面的性能.
- 为了确定与低血糖症相关的关键临床因素,在这个患者群体.
主要方法:
- 分析来自糖尿病住院患者的EHR数据,包括人口统计数据,实验室结果,治疗点 (POC) 血糖和程序.
- 开发LR和ANN模型,使用住院0-2天的数据来预测3-6天的低血糖症.
- 使用曲线下的面积 (AUC) 评估模型性能,并为概率可视化构建了一个名图.
主要成果:
- 该LR模型确定了重要的预测因素:胰岛素使用,先前低血糖,呼吸率,血液尿素,,D-二次数,血糖系数变化 (BG CV) >31%,血糖差距 (BG gap) >10 mmol/L.
- 两种LR (AUC=0.763) 和ANN (AUC=0.762) 模型都显示出可比的预测性能.
- 基于LR模型的命名图与观察到的临床结果有很好的一致性,决策曲线分析 (DCA) 表明临床效用.
结论:
- 后勤回归模型是预测糖尿病住院患者低血糖风险的一个有价值的工具,其性能与ANN相比.
- 建议进行进一步的临床试验,以评估该模型在减少住院患者低血糖事件方面的实际影响.
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