整合机器学习和门德尔随机化确定了川崎病冠状动脉损伤的因果实验室生物标志物:一项前性研究
Hancao Yang1, Meng Wu2, Keqing Liang1
1Department of Clinical Laboratory, Children's Hospital of Fudan University & National Children Medical Center, Shanghai, China.
Frontiers in genetics
|September 2, 2025
概括
人工智能模型可以预测卡瓦萨基病 (KD) 的儿童冠状动脉损伤 (CAL) 风险. 这种经过基因分析验证的方法有助于对KD患者的早期干预.
科学领域:
- 儿童心脏病学
- 医学的人工智能
- 遗传学
背景情况:
- 川崎病 (KD) 在儿童中具有显著的冠状动脉病变 (CAL) 风险.
- 早期识别高危患者对于及时干预和改善结果至关重要.
研究的目的:
- 开发和验证人工智能模型,用于预测KAL患者的风险.
- 确定关键的预测特征及其与CAL的因果关系.
主要方法:
- 使用机器学习 (ML) 模型,包括多层感知子分类器 (MLPC),随机森林 (RF) 和额外树 (ET),对506名KD患者的数据.
- 进行跨时间验证和门德尔随机化 (MR) 分析以评估模型性能和遗传因果关系.
- 确定了用于模型开发的七个预测特征.
主要成果:
- 在验证的ML模型中,MLPC显示了最高的准确性.
- 激活的部分血小板时间 (APTT) 和红细胞分布宽度 (RDW) 被确定为与CAL相关的因果因素.
- 在基因验证的支持下,人工智能模型提供了可靠的风险分层.
结论:
- 基于人工智能的预测模型与MR的遗传见解相结合,为KD早期CAL风险评估提供了可靠的方法.
- 这种策略可以促进临床管理,并可能减少严重并发症的发生率.
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