SMART:一种新的患者相似性估计框架,用于在急性损伤中进行增强的预测建模
Deyi Li1, Alan S L Yu2, Dana Y Fuhrman3,4
1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32611, United States.
Journal of the American Medical Informatics Association : JAMIA
|August 12, 2025
概括
智能框架准确地识别了使用电子健康记录的类似患者,改善了对急性损伤 (AKI) 风险的个性化预测. 这提高了治疗量身定制,并推进了精准医学.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 患者相似性测量对于精准医学和个性化医疗保健至关重要.
- 准确预测急性损伤 (AKI) 对及时的临床干预至关重要.
- 电子健康记录 (EHR) 为患者相似性分析提供了丰富的数据.
研究的目的:
- 开发基于EHR的患者相似性估计框架,用于增强个人化AKI预测建模.
- 提高识别类似患者队列的准确性,以实现个性化治疗策略.
- 通过先进的预测分析来解决AKI的复杂性和危及生命的性质.
主要方法:
- 引入急性损伤风险跟踪 (SMART) 框架的相似性测量.
- 实施关键改进:重叠权重,距离测量优化和功能类型重量优化.
- 使用来自两个高等学术医院的内部和外部验证数据集进行评估.
主要成果:
- 在内部和外部测试集 (P<.05) 中,SMART的表现始终优于三个基线模型的表现.
- 该框架在具有独特健康特征的特定亚群中显示出更好的预测性能.
- 分析涉及参考池中的8637名患者和每个测试组中的8542名患者.
结论:
- 智能显著提高识别高质量的类似的患者群体的AKI预测.
- 该框架提高了个性化AKI风险预测在不同患者队伍的准确性.
- 准确识别相似的患者有助于更有效,量身定制的临床治疗,并推进个性化护理.
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