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在多个医院学习竞争的风险:一次性分布式算法
Dazheng Zhang1,2, Jiayi Tong1,2, Naimin Jing2,3
1The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States.
概括
我们开发了新的算法 (ODACoR) 来分析儿童复杂的临床状况,在准确性方面表现优于传统方法,并确定SARS-CoV-2 (PASC) 后急性后续症的关键风险因素.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 分析多种临床条件的相互作用,特别是在儿科患者群体中,带来了重大的统计挑战.
- 在儿童和青少年中,SARS-CoV-2 (PASC) 后急性后续症需要强有力的方法来识别风险因素并了解疾病的进展.
- 现有的方法,如元分析,可能与罕见事件和来自多机构电子健康记录的复杂数据结构作斗争.
研究的目的:
- 为竞争性风险模型 (ODACoR) 开发新的分布式算法.
- 在时间到事件分析中描述多种临床条件的相互作用.
- 使用多医院EHR数据量化儿童和青少年PASC的风险因素.
主要方法:
- 为竞争性风险模型 (ODACoR) 开发了两个一次性分布式算法.
- 将ODACoR应用于来自八个国家儿童医院的EHR数据.
- 与元分析和聚合数据估计器对比评估算法准确性.
主要成果:
- 对于罕见疾病,ODACoR算法显示相对偏差 (∼0.2%) 与元分析 (∼40%) 相比明显较低.
- ODACoR的估计与聚合数据的估计相同,这表明其可靠性很高.
- ODACoR确定了PASC (年龄,性别,慢性疾病,肥胖症) 的关键风险因素,这些风险因素被元分析遗漏了.
结论:
- 拟议的ODACoR算法具有通信效率,高度准确,并适合表征复杂的临床条件相互作用.
- ODACoR提供了一个强大的,可扩展的解决方案,用于多机构的时间到事件分析,适用于各种临床研究问题.
- 这种方法提高了分析大规模儿科EHR数据的能力,以更好地了解疾病风险和结果.
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