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) Theme Issue: Medical Informatics and COVID-19 (103) Decision Support for Health Professionals (1137) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1482) Secondary Use of Clinical Data for Research and Surveillance (320) Infectious Diseases (non-STD/STI) (1485) Artificial Intelligence (1198) Outbreak and Pandemic Preparedness and Management (1675)

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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26211, first published .
Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation

Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation

Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation

Authors of this article:

Juan L Domínguez-Olmedo1 Author Orcid Image ;   Álvaro Gragera-Martínez2 Author Orcid Image ;   Jacinto Mata1 Author Orcid Image ;   Victoria Pachón Álvarez1 Author Orcid Image
Article Authors Cited by (21) Tweetations (5) Metrics

Journals

  1. 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
  2. Calabuig J, Jiménez-Fernández E, Sánchez-Pérez E, Manzanares S. Modeling Hospital Resource Management during the COVID-19 Pandemic: An Experimental Validation. Econometrics 2021;9(4):38 View
  3. Jung C, Mamandipoor B, Fjølner J, Bruno R, Wernly B, Artigas A, Bollen Pinto B, Schefold J, Wolff G, Kelm M, Beil M, Sviri S, van Heerden P, Szczeklik W, Czuczwar M, Elhadi M, Joannidis M, Oeyen S, Zafeiridis T, Marsh B, Andersen F, Moreno R, Cecconi M, Leaver S, De Lange D, Guidet B, Flaatten H, Osmani V. Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation. JMIR Medical Informatics 2022;10(3):e32949 View
  4. Kiss O, Alzueta E, Yuksel D, Pohl K, de Zambotti M, Műller-Oehring E, Prouty D, Durley I, Pelham W, McCabe C, Gonzalez M, Brown S, Wade N, Marshall A, Sowell E, Breslin F, Lisdahl K, Dick A, Sheth C, McCandliss B, Guillaume M, Van Rinsveld A, Dowling G, Tapert S, Baker F. The Pandemic's Toll on Young Adolescents: Prevention and Intervention Targets to Preserve Their Mental Health. Journal of Adolescent Health 2022;70(3):387 View
  5. Rojas-García M, Vázquez B, Torres-Poveda K, Madrid-Marina V. Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach. BMC Infectious Diseases 2023;23(1) View
  6. Nopour R, Shanbezadeh M, Kazemi-Arpanahi H. Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks. Journal of Education and Health Promotion 2023;12(1):16 View
  7. Wang H, Ai H, Fu Y, Li Q, Cui R, Ma X, Ma Y, Wang Z, Liu T, Long Y, Qu K, Liu C, Zhang J. Development of an Early Warning Model for Predicting the Death Risk of Coronavirus Disease 2019 Based on Data Immediately Available on Admission. Frontiers in Medicine 2021;8 View
  8. Limam H, Hasni O, Alaya I. A novel hybrid approach for feature selection enhancement: COVID-19 case study. Computer Methods in Biomechanics and Biomedical Engineering 2023;26(10):1183 View
  9. Varzaneh Z, Orooji A, Erfannia L, Shanbehzadeh M. A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method. Informatics in Medicine Unlocked 2022;28:100825 View
  10. Nwanosike E, Conway B, Merchant H, Hasan S. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. International Journal of Medical Informatics 2022;159:104679 View
  11. Wen X, Leng P, Wang J, Yang G, Zu R, Jia X, Zhang K, Mengesha B, Huang J, Wang D, Luo H. Clinlabomics: leveraging clinical laboratory data by data mining strategies. BMC Bioinformatics 2022;23(1) View
  12. Antoñanzas J, Perramon A, López C, Boneta M, Aguilera C, Capdevila R, Gatell A, Serrano P, Poblet M, Canadell D, Vilà M, Catasús G, Valldepérez C, Català M, Soler-Palacín P, Prats C, Soriano-Arandes A. Symptom-Based Predictive Model of COVID-19 Disease in Children. Viruses 2021;14(1):63 View
  13. Yang D, Zhao L, Kang J, Wen C, Li Y, Ren Y, Wang H, Zhang S, Yang S, Song J, Gao D, Li Y. Development and validation of a predictive model for acute kidney injury in patients with moderately severe and severe acute pancreatitis. Clinical and Experimental Nephrology 2022;26(8):770 View
  14. González-Cebrián A, Borràs-Ferrís J, Ordovás-Baines J, Hermenegildo-Caudevilla M, Climente-Marti M, Tarazona S, Vitale R, Palací-López D, Sierra-Sánchez J, Saez de la Fuente J, Ferrer A, Camps J. Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients. PLOS ONE 2022;17(9):e0274171 View
  15. Morís D, de Moura J, Marcos P, Rey E, Novo J, Ortega M. Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models. Biomedical Signal Processing and Control 2023;84:104818 View
  16. Ovchinnikova M, Zhilenkova Y, Chernysh N. Аpplication of Big Data in laboratory medicine. Russian Journal for Personalized Medicine. Russian Journal for Personalized Medicine 2023;3(4):77 View
  17. Chadaga K, Prabhu S, Sampathila N, Chadaga R. Severity prediction in COVID-19 patients using clinical markers and explainable artificial intelligence: A stacked ensemble machine learning approach. Intelligent Decision Technologies 2023;17(4):959 View
  18. Casas-Rojo J, Ventura P, Antón Santos J, de Latierro A, Arévalo-Lorido J, Mauri M, Rubio-Rivas M, González-Vega R, Giner-Galvañ V, Otero Perpiñá B, Fonseca-Aizpuru E, Muiño A, Del Corral-Beamonte E, Gómez-Huelgas R, Arnalich-Fernández F, Llorente Barrio M, Sancha-Lloret A, Rábago Lorite I, Loureiro-Amigo J, Pintos-Martínez S, García-Sardón E, Montaño-Martínez A, Rojano-Rivero M, Ramos-Rincón J, López-Escobar A. Improving prediction of COVID-19 mortality using machine learning in the Spanish SEMI-COVID-19 registry. Internal and Emergency Medicine 2023;18(6):1711 View
  19. Ghaderzadeh M, Asadi F, Ramezan Ghorbani N, Almasi S, Taami T. Toward artificial intelligence (AI) applications in the determination of COVID-19 infection severity: considering AI as a disease control strategy in future pandemics. Iranian Journal of Blood and Cancer 2023;15(3):93 View
  20. Badiola-Zabala G, Lopez-Guede J, Estevez J, Graña M. Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022. Electronics 2024;13(6):1005 View

Books/Policy Documents

  1. Magboo V, Magboo M. Innovation in Medicine and Healthcare. View