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

JMIR Theme Issue: COVID-19 Special Issue (2388) Artificial Intelligence (1190) Machine Learning (1374) Decision Support for Health Professionals (1130) New Methods (903) Infectious Diseases (non-STD/STI) (1481) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1482) Clinical Information and Decision Making (1317) Outbreak and Pandemic Preparedness and Management (1673) Theme Issue: Medical Informatics and COVID-19 (103)

Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23026, first published .
Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

Authors of this article:

Shengtian Sang1 Author Orcid Image ;   Ran Sun1 Author Orcid Image ;   Jean Coquet1 Author Orcid Image ;   Harris Carmichael2 Author Orcid Image ;   Tina Seto3 Author Orcid Image ;   Tina Hernandez-Boussard1 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

Journals

  1. Carmichael H, Coquet J, Sun R, Sang S, Groat D, Asch S, Bledsoe J, Peltan I, Jacobs J, Hernandez-Boussard T. Learning from past respiratory failure patients to triage COVID-19 patient ventilator needs: A multi-institutional study. Journal of Biomedical Informatics 2021;119:103802 View
  2. Ng A, Wei B, Jain J, Ward E, Tandon S, Moskowitz J, Krogh-Jespersen S, Wakschlag L, Alshurafa N. Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation. JMIR mHealth and uHealth 2022;10(8):e33850 View
  3. Wynants L, Van Calster B, Collins G, Riley R, Heinze G, Schuit E, Albu E, Arshi B, Bellou V, Bonten M, Dahly D, Damen J, Debray T, de Jong V, De Vos M, Dhiman P, Ensor J, Gao S, Haller M, Harhay M, Henckaerts L, Heus P, Hoogland J, Hudda M, Jenniskens K, Kammer M, Kreuzberger N, Lohmann A, Levis B, Luijken K, Ma J, Martin G, McLernon D, Navarro C, Reitsma J, Sergeant J, Shi C, Skoetz N, Smits L, Snell K, Sperrin M, Spijker R, Steyerberg E, Takada T, Tzoulaki I, van Kuijk S, van Bussel B, van der Horst I, Reeve K, van Royen F, Verbakel J, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons K, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020:m1328 View
  4. Parvin S, Islam M, Majumdar T, Ahmed F. Clinicodemographic profile, intensive care unit utilization and mortality rate among COVID-19 patients admitted during the second wave in Bangladesh. IJID Regions 2022;2:55 View
  5. Zhou Y, Feng J, Mei S, Tang R, Xing S, Qin S, Zhang Z, Xu Q, Gao Y, He Z. A deep learning model for predicting COVID-19 ARDS in critically ill patients. Frontiers in Medicine 2023;10 View
  6. Ji B, Kong L, Wang J, Liu C, Yuan K, Zhu L, Liang H. Predicting the prognosis of patients with mild COVID-19 by chest CT based on machine learning. Chinese Journal of Academic Radiology 2024;7(2):157 View
  7. Zhang L, Xu J, Li Y, Meng F, Wang W. Smoking on the risk of acute respiratory distress syndrome: a systematic review and meta-analysis. Critical Care 2024;28(1) View
  8. El Emam K, Leung T, Malin B, Klement W, Eysenbach G. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS). Journal of Medical Internet Research 2024;26:e52508 View