Machine learning forecasting for COVID-19 pandemic-associated effects on paediatric respiratory infections.
Machine learning forecasting for COVID-19 pandemic-associated effects on paediatric respiratory infections.
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DOI：
10.1136/archdischild-2022-323822
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发表时间：
2022-12
影响因子：
5.2
通讯作者：
中科院分区：
文献类型：
--
作者：
COVID-19大流行和随后的政府限制对医疗服务和疾病传播，特别是与急性呼吸道感染相关的疾病传播产生了重大影响。本研究检查了来自英国英格兰一家专科儿童医院的不可识别的常规电子病历数据，与基于开源、可转移的机器学习模型的预测相比，研究了大流行缓解措施对季节性呼吸道感染率的影响。我们对2010年1月至2022年2月期间的呼吸系统疾病诊断进行了回顾性纵向研究。所有诊断均来自常规医疗保健活动数据，并计算几个诊断组的诊断率。为了研究诊断的变化，季节预报模型拟合前限制期数据并外推。根据来自31 002名患者的144 704份诊断，除两个诊断组外，所有诊断组在限制期间的诊断率都显着降低。我们观察到，“呼吸道合胞病毒”、“流感”、“急性鼻咽炎”和“急性细支气管炎”的高峰诊断率分别降低了91%、89%、72%和63%。机器学习预测模型计算出，与限制期间的预期相比，总诊断减少了高达73% (z-score: - 26)，限制后增加了高达27% （z-score: 8）。我们证明了与COVID-19相关的限制与儿科季节性呼吸道感染的显著减少之间的关联。此外，虽然许多感染率已恢复到限制措施后的预期水平，但其他感染率仍然受到抑制或遵循非典型的冬季趋势。本研究进一步证明了常规电子病历数据和跨域时间序列预测在建模、监测、分析和解决临床重要问题方面的适用性和有效性。回顾性研究机器学习预测Covid - 19大流行对儿科呼吸道病毒的相关影响。
The COVID-19 pandemic and subsequent government restrictions have had a major impact on healthcare services and disease transmission, particularly those associated with acute respiratory infection. This study examined non-identifiable routine electronic patient record data from a specialist children’s hospital in England, UK, examining the effect of pandemic mitigation measures on seasonal respiratory infection rates compared with forecasts based on open-source, transferable machine learning models. We performed a retrospective longitudinal study of respiratory disorder diagnoses between January 2010 and February 2022. All diagnoses were extracted from routine healthcare activity data and diagnosis rates were calculated for several diagnosis groups. To study changes in diagnoses, seasonal forecast models were fit to prerestriction period data and extrapolated. Based on 144 704 diagnoses from 31 002 patients, all but two diagnosis groups saw a marked reduction in diagnosis rates during restrictions. We observed 91%, 89%, 72% and 63% reductions in peak diagnoses of ‘respiratory syncytial virus’, ‘influenza’, ‘acute nasopharyngitis’ and ‘acute bronchiolitis’, respectively. The machine learning predictive model calculated that total diagnoses were reduced by up to 73% (z-score: −26) versus expected during restrictions and increased by up to 27% (z-score: 8) postrestrictions. We demonstrate the association between COVID-19 related restrictions and significant reductions in paediatric seasonal respiratory infections. Moreover, while many infection rates have returned to expected levels postrestrictions, others remain supressed or followed atypical winter trends. This study further demonstrates the applicability and efficacy of routine electronic record data and cross-domain time-series forecasting to model, monitor, analyse and address clinically important issues. Retrospective study looking at Machine Learning Forecasting for Covid 19 Pandemic Associated effects on paediatric respiratory viruses.
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影响因子：
15.2
作者：
Brat, Gabriel A.;Weber, Griffin M.;Kohane, Isaac S.
通讯作者：
Kohane, Isaac S.
DOI：
10.1016/j.jcv.2022.105126
发表时间：
2022-04
期刊：
Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology
影响因子：
--
作者：
Dolores A;Stephanie G;Mercedes S NJ;Érica G;Mistchenko AS;Mariana V
通讯作者：
Mariana V
影响因子：
5.1
作者：
Dash S;Chakraborty C;Giri SK;Pani SK
通讯作者：
Pani SK
在2019 - 2020年，零星病毒感染的零星诺如病毒感染减少，费城。
影响因子：
5.4
作者：
Nachamkin I;Richard-Greenblatt M;Yu M;Bui H
通讯作者：
Bui H
影响因子：
4.8
作者：
Liu P;Xu M;Cao L;Su L;Lu L;Dong N;Jia R;Zhu X;Xu J
通讯作者：
Xu J