This paper is in the following e-collection/theme issue:

Theme Issue: Medical Informatics and COVID-19 (103) JMIR Theme Issue: COVID-19 Special Issue (2388) Machine Learning (1370) Clinical Information and Decision Making (1314) Decision Support for Health Professionals (1127) Electronic Health Records (961) Secondary Use of Clinical Data for Research and Surveillance (317) COVID-19 in an Elderly Population (11)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32949, first published .
Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation

Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation

Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation

Authors of this article:

Christian Jung1 Author Orcid Image ;   Behrooz Mamandipoor2 Author Orcid Image ;   Jesper Fjølner3 Author Orcid Image ;   Raphael Romano Bruno1 Author Orcid Image ;   Bernhard Wernly4 Author Orcid Image ;   Antonio Artigas5 Author Orcid Image ;   Bernardo Bollen Pinto6 Author Orcid Image ;   Joerg C Schefold7 Author Orcid Image ;   Georg Wolff1 Author Orcid Image ;   Malte Kelm1 Author Orcid Image ;   Michael Beil8 Author Orcid Image ;   Sigal Sviri8 Author Orcid Image ;   Peter V van Heerden9 Author Orcid Image ;   Wojciech Szczeklik10 Author Orcid Image ;   Miroslaw Czuczwar11 Author Orcid Image ;   Muhammed Elhadi12 Author Orcid Image ;   Michael Joannidis13 Author Orcid Image ;   Sandra Oeyen14 Author Orcid Image ;   Tilemachos Zafeiridis15 Author Orcid Image ;   Brian Marsh16 Author Orcid Image ;   Finn H Andersen17, 18 Author Orcid Image ;   Rui Moreno19, 20 Author Orcid Image ;   Maurizio Cecconi21 Author Orcid Image ;   Susannah Leaver22 Author Orcid Image ;   Dylan W De Lange23 Author Orcid Image ;   Bertrand Guidet24, 25 Author Orcid Image ;   Hans Flaatten26, 27 Author Orcid Image ;   Venet Osmani2 Author Orcid Image
Article Authors Cited by (5) Tweetations (2) Metrics

Journals

  1. Mamandipoor B, Bruno R, Wernly B, Wolff G, Fjølner J, Artigas A, Pinto B, Schefold J, Kelm M, Beil M, Sigal S, Leaver S, De Lange D, Guidet B, Flaatten H, Szczeklik W, Jung C, Osmani V, Pani D. 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 Digital Health 2022;1(11):e0000136 View
  2. Heyl J, Hardy F, Tucker K, Hopper A, Marchã M, Navaratnam A, Briggs T, Yates J, Day J, Wheeler A, Eve-Jones S, Gray W. Frailty, Comorbidity, and Associations With In-Hospital Mortality in Older COVID-19 Patients: Exploratory Study of Administrative Data. Interactive Journal of Medical Research 2022;11(2):e41520 View
  3. Moradi H, Bunnell H, Price B, Khodaverdi M, Vest M, Porterfield J, Anzalone A, Santangelo S, Kimble W, Harper J, Hillegass W, Hodder S, Chatterjee B. Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. PLOS ONE 2023;18(3):e0282587 View
  4. Rodríguez-Belenguer P, Piñana J, Sánchez-Montañés M, Soria-Olivas E, Martínez-Sober M, Serrano-López A. A machine learning approach to identify groups of patients with hematological malignant disorders. Computer Methods and Programs in Biomedicine 2024;246:108011 View
  5. Malaguti M, Gios L, Giometto B, Longo C, Riello M, Ottaviani D, Pellegrini M, Di Giacopo R, Donner D, Rozzanigo U, Chierici M, Moroni M, Jurman G, Bincoletto G, Pardini M, Bacchin R, Nobili F, Di Biasio F, Avanzino L, Marchese R, Mandich P, Garbarino S, Pagano M, Campi C, Piana M, Marenco M, Uccelli A, Osmani V, Fionda B. Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study. PLOS ONE 2024;19(3):e0300127 View