使用电子健康记录基础模型预测与抗生素相关的皮肤不良药物反应
Junmo Kim1, Kyunghoon Kim2,3, Jeong-Eun Yun4
1Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea.
NPJ digital medicine
|March 3, 2026
概括
本研究引入了使用电子健康记录基础模型对抗生素相关皮肤不良药反应 (CADRs) 的新预测模型. 该模型显示高精度,为预测这些常见药物反应提供了一个有希望的工具.
科学领域:
- 药物监督 药物监督 药物监督
- 医疗信息学 医疗信息学
- 临床预测模型临床预测模型
背景情况:
- 皮肤药物不良反应 (CADRs) 是常见的,可以从轻度到重度.
- 目前缺乏针对抗生素相关的CADR的有效预测工具.
- 电子健康记录 (EHR) 包含大量与ADR预测相关的患者数据.
研究的目的:
- 开发和评估一种针对抗生素相关的CADR的新型预测模型.
- 利用在EHR数据上训练的基础模型 (FMs) 来改进ADR预测.
- 评估模型在分类即时和延迟CADR类型中的性能.
主要方法:
- 利用了从语言模型范式中调整的EHR基础模型 (FMs).
- 处理了来自韩国三家医院802,131名住院患者的EHR数据,包括医疗代码,护理陈述和报告.
- 提取了皮疹记录和训练有素的预测模型,将性能与基线方法进行比较.
主要成果:
- 与所有基线模型相比,提出的EHR FM方法显示出优异的预测性能.
- 该模型在不同数据集中有效预测了与抗生素相关的CADR.
- 部分分析显示,在预测延迟型CADR方面具有特别的有效性.
结论:
- 电子心脏病理基础模型提供了一种强大而有效的方法来预测与抗生素相关的CADR.
- 这种方法具有显著的临床应用潜力,特别是在延迟反应方面.
- 这项研究强调了将多样化的EHR数据与先进的AI模型集成为药监测的价值.
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