在急性爱尔兰医院探索呼吸道感染 (2016-2021)
Doaa Amin1, Gerry Hughes2, Akke Vellinga1
1CARA Network, School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.
Journal of infection and public health
|September 21, 2025
概括
在COVID-19大流行期间,呼吸道感染 (RTIs) 导致住院时间更长,死亡率增加. 机器学习准确地预测了RTI诊断,识别了喘息和吸烟等关键因素.
科学领域:
- 传染性疾病 传染性疾病
- 公共卫生 公共卫生
- 医疗保健中的机器学习
背景情况:
- 呼吸道感染 (RTIs) 是全球疾病和死亡的重要原因.
- COVID-19大流行影响了包括RTIs在内的各种感染的医疗模式.
- 了解疫情期间RTI特征的变化对于公共卫生战略至关重要.
研究的目的:
- 在COVID-19大流行之前和期间分析RTIs的住院患者的特征.
- 应用机器学习模型来预测疫情期间的RTI诊断.
- 确定区分COVID-19和其他RTIs的因素.
主要方法:
- 利用了来自 55 家爱尔兰急性病医院 (2016-2021) 的爱尔兰医院住院调查 (HIPE) 数据集.
- 提取了诊断为RTIs的住院患者的数据.
- 采用多变量后勤回归,随机森林和极端梯度增强用于分析和预测.
主要成果:
- RTIs占感染的43.3%,流行前为65.2%,流行期间为34.8%.
- 住院时间 (LOS) 从疫情前的4天增加到5天 (其他RTIs) 和6天 (COVID-19).
- 由于RTIs的住院死亡率从大流行前的3.3%上升到大流行期间的7.6% (COVID-19:11.3%).
- 机器学习确定了与COVID-19相关的呼吸短促,咳,发烧,肥胖和全身炎症反应综合征.
- 喘息,吸烟和心脏病是区分其他RTIs与COVID-19的主要因素.
结论:
- 与疫情前时期相比,在COVID-19大流行期间,RTIs患者住院的住院时间更长,死亡率更高.
- 监督机器学习有效地帮助预测RTI诊断.
- 喘息,吸烟史和先前存在的心脏病是COVID-19和其他RTIs之间的显著歧视因素.
关键词:
人工智能 (AI) 是一种人工智能.在 COVID-19 疫情中,与医疗保健相关的感染.医院获得的感染 (HAI)机器学习 (ML) 是指机器学习.预测 预测 预测呼吸道感染 (RTIs) 是一种呼吸道感染.有监督的机器学习.更多相关视频
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