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

JMIR Theme Issue: COVID-19 Special Issue (2087) Artificial Intelligence (665) Machine Learning (1004) Clinical Information and Decision Making (902) Infectious Diseases (non-STD/STI) (1223) Outbreak and Pandemic Preparedness and Management (1453) Decision Support for Health Professionals (736)

Published on in Vol 23 , No 5 (2021) :May

Preprints (earlier versions) of this paper are available at , first published .
A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score

Authors of this article:

Geza Halasz 1 Author Orcid Image ;   Michela Sperti 2 Author Orcid Image ;   Matteo Villani 3 Author Orcid Image ;   Umberto Michelucci 4 Author Orcid Image ;   Piergiuseppe Agostoni 5 Author Orcid Image ;   Andrea Biagi 1 Author Orcid Image ;   Luca Rossi 1 Author Orcid Image ;   Andrea Botti 6 Author Orcid Image ;   Chiara Mari 6 Author Orcid Image ;   Marco Maccarini 7 Author Orcid Image ;   Filippo Pura 7 Author Orcid Image ;   Loris Roveda 7 Author Orcid Image ;   Alessia Nardecchia 8 Author Orcid Image ;   Emanuele Mottola 9 Author Orcid Image ;   Massimo Nolli 3 Author Orcid Image ;   Elisabetta Salvioni 5 Author Orcid Image ;   Massimo Mapelli 5 Author Orcid Image ;   Marco Agostino Deriu 2 Author Orcid Image ;   Dario Piga 7 Author Orcid Image ;   Massimo Piepoli 1 Author Orcid Image
Article Authors Cited by (19) Tweetations (2) Metrics

Journals

  1. Barbieri D, Giuliani E, Del Prete A, Losi A, Villani M, Barbieri A. How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic. International Journal of Environmental Research and Public Health 2021;18(14):7648 View
  2. Gendy M, Yuce M. Emerging Technologies Used in Health Management and Efficiency Improvement During Different Contact Tracing Phases Against COVID-19 Pandemic. IEEE Reviews in Biomedical Engineering 2023;16:38 View
  3. Giovagnoli M, Ciucciarelli S, Castrichella L, Giansanti D. Artificial Intelligence in Digital Pathology: What Is the Future? Part 2: An Investigation on the Insiders. Healthcare 2021;9(10):1347 View
  4. Raschke R, Rangan P, Agarwal S, Uppalapu S, Sher N, Curry S, Heise C, Wang Y. COVID-19 Time of Intubation Mortality Evaluation (C-TIME): A system for predicting mortality of patients with COVID-19 pneumonia at the time they require mechanical ventilation. PLOS ONE 2022;17(7):e0270193 View
  5. Bianchi F, Piroddi L, Bemporad A, Halasz G, Villani M, Piga D. Active preference-based optimization for human-in-the-loop feature selection. European Journal of Control 2022;66:100647 View
  6. Segall R, Sankarasubbu V. Survey of Recent Applications of Artificial Intelligence for Detection and Analysis of COVID-19 and Other Infectious Diseases. International Journal of Artificial Intelligence and Machine Learning 2022;12(2):1 View
  7. Smit J, van Genderen M, Reinders M, Gommers D, Krijthe J, Van Bommel J. Demystifying machine learning for mortality prediction. Critical Care 2021;25(1) View
  8. Cisterna-García A, Guillén-Teruel A, Caracena M, Pérez E, Jiménez F, Francisco-Verdú F, Reina G, González-Billalabeitia E, Palma J, Sánchez-Ferrer Á, Botía J. A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study. Scientific Reports 2022;12(1) View
  9. Islam K, Kumar J, Tan T, Reaz M, Rahman T, Khandakar A, Abbas T, Hossain M, Zughaier S, Chowdhury M. Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning. Diagnostics 2022;12(9):2144 View
  10. Tardiveau C, Monneret G, Lukaszewicz A, Cheynet V, Cerrato E, Imhoff K, Peronnet E, Bodinier M, Kreitmann L, Blein S, Llitjos J, Conti F, Gossez M, Buisson M, Yonis H, Cour M, Argaud L, Delignette M, Wallet F, Dailler F, Monard C, Brengel-Pesce K, Venet F. A 9-mRNA signature measured from whole blood by a prototype PCR panel predicts 28-day mortality upon admission of critically ill COVID-19 patients. Frontiers in Immunology 2022;13 View
  11. Halilaj I, Chatterjee A, van Wijk Y, Wu G, van Eeckhout B, Oberije C, Lambin P. Covid19Risk.ai: An Open Source Repository and Online Calculator of Prediction Models for Early Diagnosis and Prognosis of Covid-19. BioMed 2021;1(1):41 View
  12. 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
  13. Vezzoli M, Inciardi R, Oriecuia C, Paris S, Murillo N, Agostoni P, Ameri P, Bellasi A, Camporotondo R, Canale C, Carubelli V, Carugo S, Catagnano F, Danzi G, Dalla Vecchia L, Giovinazzo S, Gnecchi M, Guazzi M, Iorio A, La Rovere M, Leonardi S, Maccagni G, Mapelli M, Margonato D, Merlo M, Monzo L, Mortara A, Nuzzi V, Pagnesi M, Piepoli M, Porto I, Pozzi A, Provenzale G, Sarullo F, Senni M, Sinagra G, Tomasoni D, Adamo M, Volterrani M, Maroldi R, Metra M, Lombardi C, Specchia C. Machine learning for prediction of in-hospital mortality in coronavirus disease 2019 patients: results from an Italian multicenter study. Journal of Cardiovascular Medicine 2022;Publish Ahead of Print View
  14. 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
  15. Redzwan N, Ramli R. A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting. Risks 2022;10(10):191 View
  16. Gordon A, Govindarajan P, Bennett C, Matheson L, Kohn M, Camargo C, Kline J. External validation of the 4C Mortality Score for hospitalised patients with COVID-19 in the RECOVER network. BMJ Open 2022;12(4):e054700 View
  17. Salcedo D, Guerrero C, Saeed K, Mardini J, Calderon-Benavides L, Henriquez C, Mendoza A. Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions. Electronics 2022;11(23):4015 View
  18. SPERTI M, MALAVOLTA M, STAUNOVO POLACCO F, DELLAVALLE A, RUGGIERI R, BERGIA S, FAZIO A, SANTORO C, DERIU M. Cardiovascular risk prediction: from classical statistical methods to machine learning approaches. Minerva Cardiology and Angiology 2022;70(1) View

Books/Policy Documents

  1. Segall R. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning. View