使用多模式数据预测血液透析患者中SARS-CoV-2感染的情况
Juntao Duan1, Hanmo Li1, Xiaoran Ma1
1Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, United States.
Frontiers in nephrology
|September 7, 2023
概括
机器学习模型可以预测严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2) 感染在浸泡期间透析患者. 当地感染数据和疫苗接种状况是关键预测因素,但模型性能随着大流行而演变.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 传染性疾病 传染性疾病
- 数据科学数据科学数据科学
背景情况:
- 随着COVID-19大流行,透析患者受到了不成比例的影响.
- 对SARS-CoV-2的准确预测模型对于在透析环境中管理疫情至关重要.
- 现有的模型在时间上的有效性是不确定的.
研究的目的:
- 开发和评估一种机器学习模型,用于在潜伏期内预测透析患者的SARS-CoV-2感染.
- 为了评估模型的表现,并确定关键预测因子,随着流行病的演变.
主要方法:
- 开发了一个XGBoost机器学习模型,使用各种数据源:人口,临床,实验室,治疗,疫苗接种,社会经济和县级COVID-19数据.
- 从2020年4月到2022年2月,逐步评估模型性能.
- 研究了随着时间的推移,预测能力和风险因素的演变.
主要成果:
- 该模型在2020年4月至8月期间实现了0.75的AUROC,超过了之前的模型.
- 预测表现波动,最低的AUROC为2021年底-2022年初的0.6.
- 在20%的假阳性率下,该模型在研究期间检测到40%的阳性病例.
- 县级感染数据和疫苗接种情况是显著的预测因素;护理院住院在接种疫苗后的预测性较低.
结论:
- 随着流行病的演变,COVID-19预测模型的动态发生了变化.
- 县级感染率和疫苗接种情况对于有效的早期预测至关重要.
- 开发的模型可以在化期间识别SARS-CoV-2感染,具有临床应用的潜力.
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