一个回顾性队列分析利用增强智能在电子健康记录中表征长期COVID:精确医学框架
Zachary H Strasser1, Arianna Dagliati2, Zahra Shakeri Hossein Abad3
1Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
PLOS digital health
|July 25, 2023
概括
研究人员开发了一种使用电子健康记录来识别长期COVID-19症状的患者的新方法,称为COVID-19 (PASC) 后急性后续. 该工具有助于对临床试验的患者进行查,并了解PASC患病率.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 医疗保健中的机器学习
背景情况:
- COVID-19 (PASC) 后急性后续包括急性SARS-CoV-2感染后持续的身体和心理症状.
- 准确识别PASC患者对于临床试验招募,疾病估计和队列分析至关重要.
- 现有的方法可能无法在电子健康记录 (EHR) 中完全捕捉PASC症状的频谱.
研究的目的:
- 使用EHR数据开发和验证一条管道,用于识别患有持续PASC症状 (呼吸不全,疲劳,关节疼痛) 的患者.
- 为了利用分布式表示学习和机器学习来建模健康结果 (MLHO),发现新的EHR特征,表明PASC.
- 为了估计住院COVID-19患者中关键PASC症状的患病率.
主要方法:
- 追溯队列研究分析了来自三个医疗保健系统的30,422名住院COVID-19患者的EHR数据 (2020年3月 - 2021年2月).
- 实现分布式表示学习 (MLHO) 以加强超越标准诊断代码的PASC症状识别.
- 用气喘,疲劳和关节疼痛的积极预测值验证MLHO衍生的PASC定义.
主要成果:
- 基于MLHO的管道实现了0.73的呼吸障碍,0.74的疲劳和0.91的关节疼痛的积极预测值.
- 诊断后3个月或更长时间内PASC症状的估计患病率:25%呼吸不全,11%疲劳和13%关节疼痛.
- 该研究确定了住院COVID-19幸存者的持续症状的显著流行率.
结论:
- 在EHR数据中建立了一个验证的查和识别PASC患者的框架.
- 开发的工具提供了一种可靠的方法,用于估计住院患者中PASC患病率.
- 这种方法可以改善PASC研究,临床试验招聘和患者护理.
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