Published on in Vol 22, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://www.medrxiv.org/content/10.1101/2020.05.22.20109959v1 , first published .
Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing

Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing

Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing

Journals

  1. Ancochea J, Izquierdo J, Soriano J. Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning. Journal of Women's Health 2021;30(3):393 View
  2. Sánchez-Montañés M, Rodríguez-Belenguer P, Serrano-López A, Soria-Olivas E, Alakhdar-Mohmara Y. Machine Learning for Mortality Analysis in Patients with COVID-19. International Journal of Environmental Research and Public Health 2020;17(22):8386 View
  3. Hariyanto T, Putri C, Frinka P, Louisa J, Lugito N, Kurniawan A. Human Immunodeficiency Virus (HIV) and outcomes from coronavirus disease 2019 (COVID-19) pneumonia: A Meta-Analysis and Meta-Regression. AIDS Research and Human Retroviruses 2021 View
  4. Jimenez-Solem E, Petersen T, Hansen C, Hansen C, Lioma C, Igel C, Boomsma W, Krause O, Lorenzen S, Selvan R, Petersen J, Nyeland M, Ankarfeldt M, Virenfeldt G, Winther-Jensen M, Linneberg A, Ghazi M, Detlefsen N, Lauritzen A, Smith A, de Bruijne M, Ibragimov B, Petersen J, Lillholm M, Middleton J, Mogensen S, Thorsen-Meyer H, Perner A, Helleberg M, Kaas-Hansen B, Bonde M, Bonde A, Pai A, Nielsen M, Sillesen M. Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients. Scientific Reports 2021;11(1) View
  5. Chung H, Ko H, Kang W, Kim K, Lee H, Park C, Song H, Choi T, Seo J, Lee J. Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation. Journal of Medical Internet Research 2021;23(4):e27060 View
  6. Lybarger K, Ostendorf M, Thompson M, Yetisgen M. Extracting COVID-19 diagnoses and symptoms from clinical text: A new annotated corpus and neural event extraction framework. Journal of Biomedical Informatics 2021;117:103761 View
  7. Hariyanto T, Kurniawan A. Obstructive sleep apnea (OSA) and outcomes from coronavirus disease 2019 (COVID-19) pneumonia: a systematic review and meta-analysis. Sleep Medicine 2021;82:47 View
  8. Alballa N, Al-Turaiki I. Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review. Informatics in Medicine Unlocked 2021;24:100564 View
  9. Alsunaidi S, Almuhaideb A, Ibrahim N, Shaikh F, Alqudaihi K, Alhaidari F, Khan I, Aslam N, Alshahrani M. Applications of Big Data Analytics to Control COVID-19 Pandemic. Sensors 2021;21(7):2282 View
  10. Martos Pérez F, Gomez Huelgas R, Martín Escalante M, Casas Rojo J. Minimizing Selection and Classification Biases. Comment on “Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing”. Journal of Medical Internet Research 2021;23(5):e27142 View
  11. Izquierdo J, Soriano J. Authors’ Reply to: Minimizing Selection and Classification Biases Comment on “Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing”. Journal of Medical Internet Research 2021;23(5):e29405 View
  12. Kalra R, Dhanjal J, Meena A, Kalel V, Dahiya S, Singh B, Dewanjee S, Kandimalla R. COVID-19, Neuropathology, and Aging: SARS-CoV-2 Neurological Infection, Mechanism, and Associated Complications. Frontiers in Aging Neuroscience 2021;13 View
  13. Syrowatka A, Kuznetsova M, Alsubai A, Beckman A, Bain P, Craig K, Hu J, Jackson G, Rhee K, Bates D. Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases. npj Digital Medicine 2021;4(1) View
  14. Riswantini D, Nugraheni E, Arisal A, Khotimah P, Munandar D, Suwarningsih W. Big Data Research in Fighting COVID-19: Contributions and Techniques. Big Data and Cognitive Computing 2021;5(3):30 View
  15. Canales L, Menke S, Marchesseau S, D’Agostino A, del Rio-Bermudez C, Taberna M, Tello J. Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology. JMIR Medical Informatics 2021;9(7):e20492 View
  16. Purja S, Shin H, Lee J, Kim E. Is loss of smell an early predictor of COVID-19 severity: a systematic review and meta-analysis. Archives of Pharmacal Research 2021;44(7):725 View
  17. Iannella G, Vicini C, Lechien J, Ravaglia C, Poletti V, di Cesare S, Amicarelli E, Gardelli L, Grosso C, Patacca A, Magistrelli E, De Benedetto M, Toraldo D, Arigliani M, Cammaroto G, Meccariello G, De Vito A, Magliulo G, Greco A, de Vincentiis M, Ralli M, Pace A, Montincone V, Maniaci A, Cocuzza S, Seligardi M, di Giacinto I, Corso R. Association Between Severity of COVID-19 Respiratory Disease and Risk of Obstructive Sleep Apnea. Ear, Nose & Throat Journal 2021:014556132110297 View