Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank cohort study
Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank cohort study
Abstract The COVID-19 pandemic has resulted in over two million deaths globally. There is an urgent need for robust, scalable monitoring tools supporting resource allocation and stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank to estimate COVID-19 mortality risk in confirmed cases. We developed a random forest classification model using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess risk of mortality with disease deterioration (AUC: 0.92). The design and feature selection of the framework lends itself to deployment in remote settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across high volumes of patients with COVID-19, especially in hospital-at-home settings.
Aral Mert、Dabbah Mohammad A.、Klasmer Benjamin、Binning Emily、Labrique Alain B.、Mohan Diwakar、Reed Angus B.、Booth Adam T.C.、Plans David、Despotovic Alex、Yassaee Arrash
Huma Therapeutics LimitedHuma Therapeutics LimitedHuma Therapeutics LimitedHuma Therapeutics LimitedJohns Hopkins Bloomberg School Public HealthJohns Hopkins Bloomberg School Public HealthHuma Therapeutics LimitedHuma Therapeutics LimitedHuma Therapeutics Limited||University of ExeterHuma Therapeutics Limited||Faculty of Medicine, University of BelgradeHuma Therapeutics Limited
医学研究方法医药卫生理论生物科学研究方法、生物科学研究技术
COVID-19SARS-CoV-2risk factorspredictionmortalitymachine learningclinical characteristicsTRIPOD
Aral Mert,Dabbah Mohammad A.,Klasmer Benjamin,Binning Emily,Labrique Alain B.,Mohan Diwakar,Reed Angus B.,Booth Adam T.C.,Plans David,Despotovic Alex,Yassaee Arrash.Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank cohort study[EB/OL].(2025-03-28)[2025-08-02].https://www.medrxiv.org/content/10.1101/2021.02.08.21251343.点此复制
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