COVID-19 machine learning model predicts outcomes in older patients from various European countries, between pandemic waves, and in a cohort of Asian, African, and American patients

Aug 6, 2026

COVID-19 remains a complex disease in terms of clinical course and outcome diversity, which complicates disease management and hospital resource allocation. Older patients often show varied symptom patterns, and existing scoring systems can be limited, creating a need for more objective and consistent decision-support methods. Machine learning has been shown to improve prognostication consistency, but prior models have struggled with generalization across different populations, waves, and small sample sizes.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages40

Reading Time1 h 41 min

AccessFree to download

Background

Clinical challenges in older patients

Need for objective decision support

Limits of existing machine learning approaches

PLOS DIGITAL HEALTH OPEN ACCESS Citation: Mamandipoor B, Bruno RR, Wernly B, Wolff G, Fjølner J, Artigas A, et al. (2022) COVID- 19 machine learning model predicts outcomes in older patients from various European countries, between pandemic waves, and in a cohort of Asian, African, and American patients. PLOS Digit Health 1(11): e0000136 . [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pdig.0000136 Editor: Danilo Pani, University of Cagliari: Universita degli Studi Di Cagliari, ITALY Received: June 29, 2022 Accepted: September 26, 2022 Published: November 8, 2022 Copyright: © 2022 Mamandipoor et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Due to ethical reasons and institutional guidelines, the data presented in the study cannot be shared publicly. Data are available to researchers with some access restrictions applied upon request. Interested researchers may contact the corresponding author for more details. Funding: The study was supported by a grant from Fondation Assistance Publique-Hˆopitaux de Paris RESEARCH ARTICLE COVID-19 machine learning model predicts outcomes in older patients from various European countries, between pandemic waves, and in a cohort of Asian, African, and American patients Behrooz Mamandipoor1, Raphael Romano Bruno2, Bernhard Wernly3,4, Georg Wolff2, Jesper Fjølner5, Antonio Artigas6, Bernardo Bollen Pinto7, Joerg C. Schefold8, Malte Kelm2, Michael Beil9, Sviri Sigal9, Susannah Leaver10, Dylan W. De Lange11, Bertrand Guidet12,13, Hans Flaatten14, Wojciech Szczeklik15, Christian Jung2‡*, VenetOsmani1‡ 1 Digital Health Centre, Fondazione Bruno Kessler Research Institute, Trento, Italy, 2 Heinrich-HeineUniversity Duesseldorf, Medical Faculty, Department of Cardiology, Pulmonology and Vascular Medicine, Duesseldorf, Germany, 3 Department of Internal Medicine, General Hospital Oberndorf, Teaching Hospital of the Paracelsus Medical University Salzburg, 5020 Salzburg, Austria, 4 Institute of General Practice, Family Medicine and Preventive Medicine, Paracelsus Medical University, Salzburg, Austria, 5 Department of Anaesthesia and Intensive Care, Viborg Regional Hospital, Viborg, Denmark, 6 Department of Intensive Care Medicine, CIBER Enfermedades Respiratorias, Corporacion Sanitaria Universitaria Parc Tauli, Autonomous University of Barcelona, Sabadell, Spain, 7 Department of Acute Medicine, Geneva University Hospitals, Geneva, Switzerland, 8 Department of Intensive Care Medicine, Inselspital, Universit¨atsspital, University of Bern, Bern, Switzerland, 9 Dept. of Medical Intensive Care, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel, 10 General Intensive care, St George’s University Hospitals NHS Foundation trust, London, United Kingdom, 11 Department of Intensive Care Medicine, University Medical Center, University Utrecht, the Netherlands, 12 Sorbonne Universit´es, UPMC Univ Paris 06, INSERM, UMR_S 1136, Institut Pierre Louis d’Epid´emiologie et de Sant´e Publique, Equipe: ´epid´emiologie hospitalière qualit´e et organisation des soins, F-75012, Paris, France, 13 Assistance Publique—Hˆopitaux de Paris, Hˆopital Saint-Antoine, service de r´eanimation m´edicale, Paris, France, 14 Department of Clinical Medicine, University of Bergen, Department of Anaesthesia and Intensive Care, Haukeland University Hospital, Bergen, Norway, 15 Jagiellonian University Medical College, Center for Intensive Care and Perioperative Medicine, Krakow, Poland ‡ These authors are joint senior authors on this work. * [Christian.Jung@med.uni-duesseldorf.de](Christian.Jung@med.uni-duesseldorf.de) Abstract Background COVID-19 remains a complex disease in terms of its trajectory and the diversity of outcomes renderin

FAQs

Why are clinical decision-making and resource allocation challenging in COVID-19?

The disease course is complex and outcomes vary widely, making it difficult to manage patients and allocate clinical resources effectively.

What motivates the use of machine learning for prognosis in older patients?

Machine learning can enhance prognostication and improve consistency compared with limited scoring systems, especially when symptom patterns differ.

What major limitation affects current machine learning approaches?

They may not generalize well to diverse patient populations, across different pandemic waves, and they can be constrained by small sample sizes.

Aug 6, 2026

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