双层本体学增强的临床决策学习,用于首次入院死亡率预测
IEEE journal of biomedical and health informatics
|January 21, 2026
概括
预测患者在首次入院时的死亡率是具有挑战性的,因为数据有限. 双本体学增强的临床决策学习 (DOCD) 有效地使用临床知识来准确预测早期死亡率,即使是单次访问记录.
科学领域:
- 医疗保健中的人工智能
- 临床信息学 临床信息学
- 生物医学数据科学 生物医学数据科学
背景情况:
- 电子健康记录 (EHR) 的深度学习在预测医疗保健任务中表现出色.
- 由于缺乏历史患者数据,难以预测首次入院的死亡率.
- 在MIMIC-III和MIMIC-IV中,有很大比例的患者只有一次访问记录,往往直接进入ICU.
研究的目的:
- 开发一种有效的模型来预测没有先前诊断史的患者的死亡率.
- 利用临床知识来改善首次入院患者的早期死亡率预测.
- 为了应对有限的数据在预测临终病患者在初始呈现时的结果的挑战.
主要方法:
- 拟议的双本体学增强的临床决策学习 (DOCD) 模型.
- 利用双重本体学习来从诊断和程序分类学中提取层次表示.
- 实施了先验指导的注意力机制,用于知识集成的基于概率的规范化.
- 采用信息融合,将人口统计数据,生命体征和知识增强的医疗代码结合起来.
主要成果:
- 在MIMIC-III (AUROC: 0.9528,AUPRC: 0.8971) 和MIMIC-IV (AUROC: 0.9817,AUPRC: 0.8857) 数据集上,DOCD取得了卓越的性能.
- 与现有的基线模型相比显著改善,用于首次入院死亡率预测.
- 提供可解释的可视化,与已建立的临床知识保持一致.
结论:
- DOCD有效地解决了在患者病史有限的情况下对死亡率预测的挑战.
- 该模型的临床知识整合提高了预测准确性和可解释性.
- 在重症监护机构中,DOCD提供了一种有前途的方法,可以及时进行干预,并改善患者的治疗结果.
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