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使用来自电子健康记录的机器学习算法预测多囊卵巢综合征 (PCOS)

Zahra Zad, Victoria S Jiang, Amber T Wolf

medRxiv : the preprint server for health sciences

|August 14, 2023

概括

机器学习模型可以预测使用电子健康记录的女性的多囊卵巢综合征 (PCOS) 风险. 这种方法有助于早期诊断和干预,可能减少风险人群的长期健康问题.

科学领域:

生殖内分泌学生殖内分泌学
医疗信息学医学信息学
机器学习在医疗保健中的应用

背景情况:

现有的多囊卵巢综合征 (PCOS) 预测模型通常使用小样本大小,并且仅限于特定的患者群体.
早期诊断PCOS对于及时干预和管理相关健康风险至关重要.

研究的目的:

开发和验证机器学习模型,用于在大量门诊患者群体中预测PCOS风险.
为了促进PCOS的早期诊断,特别是在符合诊断标准但尚未正式诊断的个体中.

主要方法:

从2003年至2016年使用电子健康记录 (EHR) 的回顾性队列研究.
包括30601名年龄在18-45岁的女性,没有并发性内分泌不良.
开发四种机器学习模型 (逻辑回归,SVM,梯度增强树木,随机森林) 以基于诊断标准预测PCOS,包括不规则的月经,高雄性,多囊性卵巢形态.

主要成果:

机器学习模型实现了高的预测准确性,在样本外测试组中,曲线下的面积 (AUC) 从80%到85%不等.
诊断PCOS的关键预测因素包括荷尔蒙水平和肥胖.
发现的负预测因素是怀孕和阳性hCG测试.

结论:

应用于EHR数据的机器学习算法可以有效地预测大量风险人群中的PCOS风险.
这种人工智能驱动的方法可以支持在综合医疗保健系统中早期检测PCOS,从而实现及时的咨询和干预.
建议对这些模型在不同的医院群体进行进一步验证,以确保可通用性.

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