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基于机器学习构建急性乳腺炎的预测模型

Liujing Zhu¹, Zuyan Huang², Yan Chen³

¹Department of Galactophore, Liuzhou Hospital, Guangzhou Women and Children's Medical Center, Liuzhou, Guangxi, China.

Scientific reports

|August 22, 2025

概括

机器学习模型可以准确预测女性急性乳腺炎的风险. 年龄,破裂乳头,CRP,中性细胞和白细胞等关键指标有助于识别高危人群,及时进行干预.

科学领域:

医疗信息学
女性健康
传染性疾病

背景情况:

急性哺乳期乳腺炎是影响哺乳期妇女的常见并发症,具有复杂的病因和非特异性的早期症状.
延迟诊断可能导致严重感染和延长恢复,强调需要更好地识别风险.
目前对哺乳期乳腺炎风险因素的研究尚不完整.

研究的目的:

使用机器学习开发和验证哺乳期妇女急性乳腺炎风险的预测模型.
确定影响急性哺乳期乳腺炎的关键风险因素.
提供及时临床干预和准确诊断的工具.

主要方法:

一项回顾性病例对照研究,涉及369名急性乳腺炎患者和447名健康对照患者.
收集的数据包括患者的人口统计和临床指标,如年龄,平价和C反应蛋白 (CRP).
机器学习算法 (逻辑回归,天真贝叶斯,XGBoost,多层感知器) 用于构建和测试预测模型.

主要成果:

多层感知器 (MLP) 模型表现出优异的性能,AUROC为0.898,准确度为0.840,灵敏度为0.820和特异性为0.863.
发现有5个指标与急性哺乳期乳腺炎有显著关联:年龄,乳头破裂,CRP,中性粒细胞 (NE) 和白细胞 (WBC).
决策曲线分析证实了MLP模型在各种值范围内具有临床效用.

结论:

通过机器学习成功开发了急性哺乳期乳腺炎的强大预测模型.
预测乳腺炎发生的关键风险因素包括年龄,乳头破裂,CRP,NE和WBC水平.
开发的模型和确定的因素为乳腺炎的早期检测和有针对性的治疗提供了宝贵的见解.

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