Prediction of COVID-19 severity using machine learning

Kanita Karaduzovic-Hadziabdic; Muhamed Adilovic; Lu Zhang; Andrew I Lumley; Pranay Shah; Muhammad Shoaib; Venkata Satagopam; Prashant Kumar Srivastava; Costanza Emanueli; Simona Greco; Alisia Madè; Teresa Padro; Pedro Domingo; Mitja Lustrek; Markus Scholz; Maciej Rosolowski; Marko Jordan; Bettina Benczik; Bence Ágg; Péter Ferdinandy; Andrew H Baker; Guy Fagherazzi; Markus Ollert; Joanna Michel; Gabriel Sanchez; Hüseyin Firat; Timo Brandenburger; Fabio Martelli; Lina Badimon; Yvan Devaux

doi:10.1002/ctm2.70042

Clinical and Translational Medicine ›› 2024, Vol. 14 ›› Issue (10) : e70042 DOI: 10.1002/ctm2.70042

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Prediction of COVID-19 severity using machine learning

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¹. Faculty of Engineering and Natural Sciences, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina

². Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg

³. Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg

⁴. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

⁵. Luxembourg Center for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg

⁶. National Heart and Lung Institute, Imperial College London, London, UK

⁷. Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, Milan, Italy

⁸. Cardiovascular Program-ICCC, Institut d’Investigació Biomèdica Sant Pau (IIB SANT PAU); CIBERCV; Autonomous University of Barcelona, Barcelona, Spain

⁹. Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia

¹⁰. Group Genetical Statistics and Biomathematical Modelling, Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany

¹¹. Cardiometabolic and HUN-REN–SU System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary

¹². Center for Pharmacology and Drug Research & Development, Semmelweis University, Budapest, Hungary

¹³. Pharmahungary Group, Szeged, Hungary

¹⁴. Centre for Cardiovascular Science, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, Scotland, UK

¹⁵. Department of Pathology, CARIM, Maastricht University, Maastricht, The Netherlands

¹⁶. Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg

¹⁷. Department of Infection and Immunity, Luxembourg Institute of Health, Esch-Sur-Alzette, Luxembourg

¹⁸. Firalis SA, Huningue, France

¹⁹. Department of Anesthesiology, University Hospital Düsseldorf, Heinrich-Heine University Duesseldorf, Moorenstr, Germany

yvan.devaux@lih.lu

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Kanita Karaduzovic-Hadziabdic, Muhamed Adilovic, Lu Zhang, Andrew I Lumley, Pranay Shah, Muhammad Shoaib, Venkata Satagopam, Prashant Kumar Srivastava, Costanza Emanueli, Simona Greco, Alisia Madè, Teresa Padro, Pedro Domingo, Mitja Lustrek, Markus Scholz, Maciej Rosolowski, Marko Jordan, Bettina Benczik, Bence Ágg, Péter Ferdinandy, Andrew H Baker, Guy Fagherazzi, Markus Ollert, Joanna Michel, Gabriel Sanchez, Hüseyin Firat, Timo Brandenburger, Fabio Martelli, Lina Badimon, Yvan Devaux. Prediction of COVID-19 severity using machine learning. Clinical and Translational Medicine, 2024, 14(10): e70042 DOI:10.1002/ctm2.70042

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References

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[1]	World Health Organization. WHO COVID-19 Dashboard. data.who.int. http://data.who.int/dashboards/covid19/cases

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[3]	Caporali A, Anwar M, Devaux Y, et al. Non-coding RNAs as therapeutic targets and biomarkers in ischaemic heart disease. Nat Rev Cardiol 2024: 1-18,

[4]	Badimon L, Devaux Y. Transcriptomics research to improve cardiovascular healthcare. Eur Heart J. 2020; 41: 3296-3298.

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[6]	Robinson EL, Emanueli C, Martelli F, et al. Leveraging non-coding RNAs to fight cardiovascular disease: the EU-CardioRNA network. Eur Heart J. 2021; 42: 4881-4883.

[7]	Badimon L, Robinson EL, Jusic A, et al. Cardiovascular RNA markers and artificial intelligence may improve COVID-19 outcome: a position paper from the EU-CardioRNA COST Action CA17129. Cardiovasc Res. 2021; 117, 1823-1840.

[8]	Firat H., Zhang L, Baksi S, et al. FIMICS: a panel of long noncoding RNAs for cardiovascular conditions. Heliyon. 2023; 9.

[9]	Karaduzović-Hadžiabdić K, Peters A. Chapter 15 – artificial intelligence in clinical decision-making for diagnosis of cardiovascular disease using epigenetics mechanisms. In Devaux Y, Robinson EL, eds., Epigenetics in Cardiovascular Disease, Vol. 24. Academic Press; 2021: 327-345.

[10]	Devaux Y, Zhang L, Lumley AI, et al. Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality. Nat Commun. 2024; 15: 4259

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2024 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.