在牛皮中使用基于常规健康记录的机器学习模型进行生物治疗的早期风险预测
Tair Lax1, Noga Fallach1, Edia Stemmer1
1Department of Molecular Biology, Ariel University, Ariel 4070000, Israel.
Journal of clinical medicine
|September 27, 2025
概括
机器学习模型可以使用电子健康记录预测牛皮患者未来的生物治疗需求. 这些模型结合了临床和实验室数据,有助于早期识别,以获得更好的患者护理.
科学领域:
- 皮肤病学 皮肤病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 牛皮是一种慢性炎症性皮肤疾病,进展不可预测.
- 早期识别需要生物治疗的患者对于管理并发症和优化护理至关重要.
- 使用电子健康记录 (EHR) 的预测建模可以帮助早期识别.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测未来的牛皮患者使用生物疗法.
- 在牛皮中确定生物治疗启动的关键预测因素.
- 评估EHR数据在牛皮管理中用于预测建模的有用性.
主要方法:
- 使用来自Clalit健康服务的EHR数据进行的回顾性研究.
- 开发和比较KNN,SVM,随机森林和物流回归模型.
- 训练模型的数据是从发病后的前五年或生物治疗前五年的数据.
- 使用AUC-ROC,精度,回忆和F1得分进行性能评估,优先考虑回忆.
主要成果:
- 性能最好的模型整合了临床,人口和实验室数据.
- 在SVM模型中,使用早期后发病数据实现AUC=0.83,回忆=0.7.
- 随机森林模型使用生物前治疗数据实现了AUC=0.93,回忆=0.95.
- 重要的预测因素包括并发症,局部治疗频率和炎症/代谢标志物.
结论:
- 基于EHR的ML模型有效预测牛皮的生物疗法使用.
- 包含常规实验室,人口统计和临床数据的模型显示出高预测性能.
- 更大的数据集和更全面的数据可能会进一步提高模型的准确性.
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