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; COVIRNA consortium (www.covirna.eu)

doi:10.1002/ctm2.70042

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 FerdinandyAndrew H Baker, Guy Fagherazzi, Markus Ollert, Joanna Michel, Gabriel Sanchez, Hüseyin Firat, Timo Brandenburger, Fabio Martelli, Lina Badimon, Yvan Devaux^*, COVIRNA consortium (www.covirna.eu)

^*Corresponding author for this work

Research output: Contribution to journal › Article › Research › peer-review

Original language	English
Article number	e70042
Journal	Clinical and Translational Medicine
Volume	14
Issue number	10
DOIs	https://doi.org/10.1002/ctm2.70042
Publication status	Published - Oct 2024

Keywords

COVID-19/diagnosis
Machine Learning
Humans
Severity of Illness Index
SARS-CoV-2
Male
Female
Middle Aged

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10.1002/ctm2.70042Licence: CC BY

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Karaduzovic-Hadziabdic, K., Adilovic, M., Zhang, L., Lumley, A. I., Shah, P., Shoaib, M., Satagopam, V., Srivastava, P. K., Emanueli, C., Greco, S., Madè, A., Padro, T., Domingo, P., Lustrek, M., Scholz, M., Rosolowski, M., Jordan, M., Benczik, B., Ágg, B., ... COVIRNA consortium (www.covirna.eu) (2024). Prediction of COVID-19 severity using machine learning. Clinical and Translational Medicine, 14(10), Article e70042. https://doi.org/10.1002/ctm2.70042

@article{b656734b33f2429fa66b82ce9f260aa8,

title = "Prediction of COVID-19 severity using machine learning",

keywords = "COVID-19/diagnosis, Machine Learning, Humans, Severity of Illness Index, SARS-CoV-2, Male, Female, Middle Aged",

author = "Kanita Karaduzovic-Hadziabdic and Muhamed Adilovic and Lu Zhang and Lumley, {Andrew I} and Pranay Shah and Muhammad Shoaib and Venkata Satagopam and Srivastava, {Prashant Kumar} and Costanza Emanueli and Simona Greco and Alisia Mad{\`e} and Teresa Padro and Pedro Domingo and Mitja Lustrek and Markus Scholz and Maciej Rosolowski and Marko Jordan and Bettina Benczik and Bence {\'A}gg and P{\'e}ter Ferdinandy and Baker, {Andrew H} and Guy Fagherazzi and Markus Ollert and Joanna Michel and Gabriel Sanchez and H{\"u}seyin Firat and Timo Brandenburger and Fabio Martelli and Lina Badimon and Yvan Devaux and {COVIRNA consortium (www.covirna.eu)}",

note = "This work was supported by the EU Horizon 2020 project COVIRNA awarded to YD (grant agreement # 101016072). The Predi-COVID study was supported by the Luxembourg National Research Fund (FNR) (Predi-COVID, grant number 14716273), the Andr{\'e} Losch Foundation and by European Regional Development Fund (FEDER, convention 2018-04-026-21). YD is funded by the EU Horizon 2020 project COVIRNA (grant agreement # 101016072), the National Research Fund (grants # C14/BM/8225223, C17/BM/11613033 and COVID-19/2020-1/14719577/miRCOVID), the Ministry of Higher Education and Research, and the Heart Foundation-Daniel Wagner of Luxembourg. FM is supported by the Italian Ministry of Health (Ricerca Corrente 2024 1.07.128, RF-2019-12368521 and POS T4 CAL.HUB.RIA cod. T4-AN-09), EU COVIRNA agree- ment #101016072, Next Generation EU PNRR M6C2 Inv. 2.1 PNRR-MAD-2022-12375790 and PNRR/2022/C9/MCID/I8 FibroThera. Horizon 2020 Framework Programme 101016072, Andr{\'e} Losch Fondation, Heart Foundation-Daniel Wagner of Luxembourg, Ministero della Salute POS T4 CAL.HUB.RIA cod. T4-AN-09, RF-2019-12368521, Ricerca Corrente 2024 1.07.128, Fonds National de la Recherche Luxembourg C14/BM/8225223, C17/BM/11613033, COVID- 19/2020-1/14719577/miRCOVID, Next Generation EU, European Regional Development Fund FEDER, convention 2018-04-026-21, Minist{\`e}re de l{\textquoteright}Education Nationale, de l{\textquoteright}Enseignement Superieur et de la Recherche. P.F. and B.{\'A}. were funded by project no. RRF-2.3.1-21-2022-00003 that has been implemented with the support provided by the European Union. The 2020-1.1.5-GYORS{\'I}T{\'O}S{\'A}V-202100011 project was funded by the Ministry for Innovation and Technology with support from the National Research Development and Innovation Fund under the 2020-1.1.5-GYORS{\'I}T{\'O}S{\'A}V call programme. This study was funded by the grant 2020-1.1.6-J{\"O}V{\H O}-2021-00013 ( “Befektet{\'e}s a j{\"O}v{\H O}be” NKFIH). This project has received funding from the HUN-REN Hungarian Research Network ",

year = "2024",

month = oct,

doi = "10.1002/ctm2.70042",

language = "English",

volume = "14",

journal = "Clinical and Translational Medicine",

issn = "2001-1326",

publisher = "John Wiley and Sons Inc",

number = "10",

}

Karaduzovic-Hadziabdic, K, Adilovic, M, Zhang, L , Lumley, AI, Shah, P, Shoaib, M, Satagopam, V, Srivastava, PK, Emanueli, C, Greco, S, Madè, A, Padro, T, Domingo, P, Lustrek, M, Scholz, M, Rosolowski, M, Jordan, M, Benczik, B, Ágg, B, Ferdinandy, P, Baker, AH, Fagherazzi, G , Ollert, M, Michel, J, Sanchez, G, Firat, H, Brandenburger, T, Martelli, F, Badimon, L, Devaux, Y & COVIRNA consortium (www.covirna.eu) 2024, 'Prediction of COVID-19 severity using machine learning', Clinical and Translational Medicine, vol. 14, no. 10, e70042. https://doi.org/10.1002/ctm2.70042

TY - JOUR

T1 - Prediction of COVID-19 severity using machine learning

AU - Karaduzovic-Hadziabdic, Kanita

AU - Adilovic, Muhamed

AU - Zhang, Lu

AU - Lumley, Andrew I

AU - Shah, Pranay

AU - Shoaib, Muhammad

AU - Satagopam, Venkata

AU - Srivastava, Prashant Kumar

AU - Emanueli, Costanza

AU - Greco, Simona

AU - Madè, Alisia

AU - Padro, Teresa

AU - Domingo, Pedro

AU - Lustrek, Mitja

AU - Scholz, Markus

AU - Rosolowski, Maciej

AU - Jordan, Marko

AU - Benczik, Bettina

AU - Ágg, Bence

AU - Ferdinandy, Péter

AU - Baker, Andrew H

AU - Fagherazzi, Guy

AU - Ollert, Markus

AU - Michel, Joanna

AU - Sanchez, Gabriel

AU - Firat, Hüseyin

AU - Brandenburger, Timo

AU - Martelli, Fabio

AU - Badimon, Lina

AU - Devaux, Yvan

AU - COVIRNA consortium (www.covirna.eu)

N1 - This work was supported by the EU Horizon 2020 project COVIRNA awarded to YD (grant agreement # 101016072). The Predi-COVID study was supported by the Luxembourg National Research Fund (FNR) (Predi-COVID, grant number 14716273), the André Losch Foundation and by European Regional Development Fund (FEDER, convention 2018-04-026-21). YD is funded by the EU Horizon 2020 project COVIRNA (grant agreement # 101016072), the National Research Fund (grants # C14/BM/8225223, C17/BM/11613033 and COVID-19/2020-1/14719577/miRCOVID), the Ministry of Higher Education and Research, and the Heart Foundation-Daniel Wagner of Luxembourg. FM is supported by the Italian Ministry of Health (Ricerca Corrente 2024 1.07.128, RF-2019-12368521 and POS T4 CAL.HUB.RIA cod. T4-AN-09), EU COVIRNA agree- ment #101016072, Next Generation EU PNRR M6C2 Inv. 2.1 PNRR-MAD-2022-12375790 and PNRR/2022/C9/MCID/I8 FibroThera. Horizon 2020 Framework Programme 101016072, André Losch Fondation, Heart Foundation-Daniel Wagner of Luxembourg, Ministero della Salute POS T4 CAL.HUB.RIA cod. T4-AN-09, RF-2019-12368521, Ricerca Corrente 2024 1.07.128, Fonds National de la Recherche Luxembourg C14/BM/8225223, C17/BM/11613033, COVID- 19/2020-1/14719577/miRCOVID, Next Generation EU, European Regional Development Fund FEDER, convention 2018-04-026-21, Ministère de l’Education Nationale, de l’Enseignement Superieur et de la Recherche. P.F. and B.Á. were funded by project no. RRF-2.3.1-21-2022-00003 that has been implemented with the support provided by the European Union. The 2020-1.1.5-GYORSÍTÓSÁV-202100011 project was funded by the Ministry for Innovation and Technology with support from the National Research Development and Innovation Fund under the 2020-1.1.5-GYORSÍTÓSÁV call programme. This study was funded by the grant 2020-1.1.6-JÖVŐ-2021-00013 ( “Befektetés a jÖvŐbe” NKFIH). This project has received funding from the HUN-REN Hungarian Research Network

PY - 2024/10

Y1 - 2024/10

KW - COVID-19/diagnosis

KW - Machine Learning

KW - Humans

KW - Severity of Illness Index

KW - SARS-CoV-2

KW - Male

KW - Female

KW - Middle Aged

UR - https://pubmed.ncbi.nlm.nih.gov/39370709/

U2 - 10.1002/ctm2.70042

DO - 10.1002/ctm2.70042

M3 - Article

C2 - 39370709

SN - 2001-1326

VL - 14

JO - Clinical and Translational Medicine

JF - Clinical and Translational Medicine

IS - 10

M1 - e70042

ER -

Prediction of COVID-19 severity using machine learning

Keywords

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