Published on in Vol 22 , No 12 (2020) :December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25442, first published .
An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model

An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model

An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model

Journals

  1. di Filippo L, Doga M, Frara S, Giustina A. Hypocalcemia in COVID-19: Prevalence, clinical significance and therapeutic implications. Reviews in Endocrine and Metabolic Disorders 2021 View
  2. Lippi G, Henry B, Favaloro E. Mean Platelet Volume Predicts Severe COVID-19 Illness. Seminars in Thrombosis and Hemostasis 2021;47(04):456 View
  3. Khan I, Aslam N, Aljabri M, Aljameel S, Kamaleldin M, Alshamrani F, Chrouf S. Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients. International Journal of Environmental Research and Public Health 2021;18(12):6429 View
  4. Lin J, Chien T, Wang L, Chou W. An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission. Medicine 2021;100(28):e26532 View
  5. Marcolino M, Pires M, Ramos L, Silva R, Oliveira L, Carvalho R, Mourato R, Sánchez-Montalvá A, Raventós B, Anschau F, Chatkin J, Nogueira M, Guimarães-Júnior M, Vietta G, Duani H, Ponce D, Ziegelmann P, Castro L, Ruschel K, Cimini C, Francisco S, Floriani M, Nascimento G, Farace B, Monteiro L, Souza-Silva M, Sales T, Martins K, Borges do Nascimento I, Fereguetti T, Ferrara D, Botoni F, Etges A, Schwarzbold A, Maurílio A, Scotton A, Weber A, Costa A, Glaeser A, Madureira A, Bhering A, de Castro B, da Silva C, Ramos C, Gomes C, de Carvalho C, Silveira D, Cezar E, Pereira E, Kroger E, Vallt F, Lucas F, Aranha F, Bartolazzi F, Crestani G, Bastos G, Madeira G, Noal H, Vianna H, Guimarães H, Gomes I, Molina I, Batista J, de Alvarenga J, Guimarães J, de Morais J, Rugolo J, Pontes K, dos Santos K, de Oliveira L, Pinheiro L, Pacheco L, Sousa L, Couto L, Kopittke L, de Moura L, Santos L, Cabral M, Souza M, Tofani M, Carneiro M, Ferreira M, Bicalho M, Lima M, Godoy M, Cardoso M, Figueiredo M, Sampaio N, Rangel N, Crespo N, de Oliveira N, Assaf P, Martelli P, Almeida R, Martins R, Lutkmeier R, Valacio R, Finger R, Cardoso R, Pozza R, Campos R, Menezes R, de Abreu R, Silva R, Guimarães S, Araújo S, Pereira S, Oliveira T, Kurtz T, de Oliveira T, Araújo T, Diniz T, dos Santos V, Gomes V, do Vale V, Ramires Y, Boersma E, Polanczyk C. ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores. International Journal of Infectious Diseases 2021;110:281 View
  6. Sankaranarayanan S, Balan J, Walsh J, Wu Y, Minnich S, Piazza A, Osborne C, Oliver G, Lesko J, Bates K, Khezeli K, Block D, DiGuardo M, Kreuter J, O’Horo J, Kalantari I, Klee E, Salama M, Kipp B, Morice II W, Jenkinson G. COVID-19 mortality prediction from deep learning in a large multistate EHR and LIS dataset: algorithm development and validation (Preprint). Journal of Medical Internet Research 2021 View