从电子健康记录中预测风险的时间自我注意,使用非静态内核近似计算
Rawan AlSaad1, Qutaibah Malluhi2, Alaa Abd-Alrazaq1
1AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar.
Artificial intelligence in medicine
|March 10, 2024
概括
在电子健康记录 (EHR) 中建模非静止性至关重要. 我们使用非静止内核的新方法显著改善了患者表现和从EHR数据预测下一个诊断.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 时间数据分析时间数据分析
背景情况:
- 电子健康记录 (EHR) 对患者代表至关重要,但对其固有的非静止性进行建模仍然是一个挑战.
- 现有的方法往往假定静止,忽视了关键的时间动态和嵌入在不规则的患者访问间隔中的领域知识.
- 疾病进展和患者状态随着时间的推移而演变,需要捕捉这些非静止模式的模型.
研究的目的:
- 引入一种新的方法,将自我注意与非静止内核近似相结合,以增强EHR患者代表性.
- 为了有效地捕捉患者访问历史中的上下文信息和复杂的时间关系.
- 解决现有的EHR建模技术中静态假设的局限性.
主要方法:
- 开发了一种新的方法,将自我注意机制与非静止的内核近似集成在一起 (例如,二次,立方,二次多项式).
- 在两个大规模,现实世界EHR数据集 (一般和孕妇患者队列) 上评估了该方法,包括超过76,000名患者.
- 将拟议的模型与基线 (例如,LSTM,RETAIN) 和静态内核近似模型进行比较,使用NDCG@10和Hit@10等指标.
主要成果:
- 非静态内核模型在两个数据集的NDCG@10和Hit@10指标上显著超过了基线方法.
- 在一般EHR数据集中,NDCG@10指标的表现得到了更实质性的改进.
- 静态内核也比基线有所提升,其表现与第二个数据集中的Hit@10的非静态内核相比.
结论:
- 这些发现强烈证实了非静态内核对EHR数据的时间建模的有效性.
- 准确建模非静止时间信息对于改善医疗预测任务至关重要.
- 拟议的方法提供了一个更强大的方法,用于从纵向EHR数据中代表患者.
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