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

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