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

JMIR Theme Issue: COVID-19 Special Issue (2391) Clinical Information and Decision Making (1328) Machine Learning (1385) Outbreak and Pandemic Preparedness and Management (1675) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1482) Decision Support for Health Professionals (1137) Artificial Intelligence (1198) Infectious Diseases (non-STD/STI) (1485) Theme Issue: Medical Informatics and COVID-19 (103) Data Science (225)

Published on in Vol 23, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26075, first published .
Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study

Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study

Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study

Authors of this article:

André Patrício1 Author Orcid Image ;   Rafael S Costa2, 3 Author Orcid Image ;   Rui Henriques1, 4 Author Orcid Image
Article Authors Cited by (11) Tweetations (1) Metrics

Journals

  1. Gonçalves D, Henriques R, Costa R. Predicting Postoperative Complications in Cancer Patients: A Survey Bridging Classical and Machine Learning Contributions to Postsurgical Risk Analysis. Cancers 2021;13(13):3217 View
  2. Abdulaal A, Patel A, Al-Hindawi A, Charani E, Alqahtani S, Davies G, Mughal N, Moore L. Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis. JMIR Formative Research 2021;5(7):e27992 View
  3. Wong K, Xiang Y, Yin L, So H. Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach. JMIR Public Health and Surveillance 2021;7(9):e29544 View
  4. He F, Page J, Weinberg K, Mishra A. The Development and Validation of Simplified Machine Learning Algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study. Journal of Medical Internet Research 2022;24(1):e31549 View
  5. Hosch R, Kattner S, Berger M, Brenner T, Haubold J, Kleesiek J, Koitka S, Kroll L, Kureishi A, Flaschel N, Nensa F. Biomarkers extracted by fully automated body composition analysis from chest CT correlate with SARS-CoV-2 outcome severity. Scientific Reports 2022;12(1) View
  6. Song W, Zhang L, Liu L, Sainlaire M, Karvar M, Kang M, Pullman A, Lipsitz S, Massaro A, Patil N, Jasuja R, Dykes P. Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods. Journal of the American Medical Informatics Association 2022;29(10):1661 View
  7. Jordan E, Shin D, Leekha S, Azarm S. Optimization in the Context of COVID-19 Prediction and Control: A Literature Review. IEEE Access 2021;9:130072 View
  8. Patrício A, Costa R, Henriques R. On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data. BMC Medical Genomics 2023;16(S1) View
  9. van Zoest V, Lindberg K, Varotsis G, Osei F, Fall T. Predicting COVID-19 hospitalizations: The importance of healthcare hotlines, test positivity rates and vaccination coverage. Spatial and Spatio-temporal Epidemiology 2024;48:100636 View
  10. Ortiz-Barrios M, Petrillo A, Arias-Fonseca S, McClean S, de Felice F, Nugent C, Uribe-López S. An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study. International Journal of Emergency Medicine 2024;17(1) View