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

JMIR Theme Issue: COVID-19 Special Issue (2453) Artificial Intelligence (1379) Machine Learning (1473) Clinical Information and Decision Making (1413) Infectious Diseases (non-STD/STI) (1576) Outbreak and Pandemic Preparedness and Management (1729) Decision Support for Health Professionals (1191)

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

Preprints (earlier versions) of this paper are available at https://www.medrxiv.org/content/10.1101/2021.03.16.21253752v1, 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 Halasz1 Author Orcid Image ;   Michela Sperti2 Author Orcid Image ;   Matteo Villani3 Author Orcid Image ;   Umberto Michelucci4 Author Orcid Image ;   Piergiuseppe Agostoni5 Author Orcid Image ;   Andrea Biagi1 Author Orcid Image ;   Luca Rossi1 Author Orcid Image ;   Andrea Botti6 Author Orcid Image ;   Chiara Mari6 Author Orcid Image ;   Marco Maccarini7 Author Orcid Image ;   Filippo Pura7 Author Orcid Image ;   Loris Roveda7 Author Orcid Image ;   Alessia Nardecchia8 Author Orcid Image ;   Emanuele Mottola9 Author Orcid Image ;   Massimo Nolli3 Author Orcid Image ;   Elisabetta Salvioni5 Author Orcid Image ;   Massimo Mapelli5 Author Orcid Image ;   Marco Agostino Deriu2 Author Orcid Image ;   Dario Piga7 Author Orcid Image ;   Massimo Piepoli1 Author Orcid Image
Article Authors Cited by (32) Tweetations (1) Metrics

Journals

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  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
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  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;23(7):439 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
  19. Emami H, Rabiei R, Sohrabei S, Atashi A. Predicting the mortality of patients with Covid‐19: A machine learning approach. Health Science Reports 2023;6(4) View
  20. Greco S, Salatiello A, Fabbri N, Riguzzi F, Locorotondo E, Spaggiari R, De Giorgi A, Passaro A. Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers. Biomedicines 2023;11(3):831 View
  21. Kaur S, Larsen E, Harper J, Purandare B, Uluer A, Hasdianda M, Umale N, Killeen J, Castillo E, Jariwala S. Development and Validation of a Respiratory-Responsive Vocal Biomarker–Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19. Journal of Medical Internet Research 2023;25:e44410 View
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  23. Wang S, Zhu R, Zhang C, Guo Y, Lv M, Zhang C, Bian C, Jiang R, Zhou W, Guo L, Sasso F. Effects of the pre-existing coronary heart disease on the prognosis of COVID-19 patients: A systematic review and meta-analysis. PLOS ONE 2023;18(10):e0292021 View
  24. Sharifi-Kia A, Nahvijou A, Sheikhtaheri A. Machine learning-based mortality prediction models for smoker COVID-19 patients. BMC Medical Informatics and Decision Making 2023;23(1) View
  25. Sadegh-Zadeh S, Sakha H, Movahedi S, Fasihi Harandi A, Ghaffari S, Javanshir E, Ali S, Hooshanginezhad Z, Hajizadeh R. Advancing prognostic precision in pulmonary embolism: A clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification. Computers in Biology and Medicine 2023;167:107696 View
  26. Cassano G, Nattino G, Belotti M, Cortellaro F, Cosentini R, Ghilardi G, Paganuzzi M, Paglia S, Rossi C, Solbiati M, Bertolini G, Brambilla A. Prognostic value of respiratory parameters for COVID-19 patients in the emergency department: results from the EC-COVID study. Internal and Emergency Medicine 2023;18(7):2075 View
  27. Shakibfar S, Nyberg F, Li H, Zhao J, Nordeng H, Sandve G, Pavlovic M, Hajiebrahimi M, Andersen M, Sessa M. Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review. Frontiers in Public Health 2023;11 View
  28. Aygun U, Yagin F, Yagin B, Yasar S, Colak C, Ozkan A, Ardigò L. Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model. Diagnostics 2024;14(5):457 View
  29. 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
  30. Kassem K, Sperti M, Cavallo A, Vergani A, Fassino D, Moz M, Liscio A, Banali R, Dahlweid M, Benetti L, Bruno F, Gallone G, De Filippo O, Iannaccone M, D'Ascenzo F, De Ferrari G, Morbiducci U, Della Valle E, Deriu M. An innovative artificial intelligence-based method to compress complex models into explainable, model-agnostic and reduced decision support systems with application to healthcare (NEAR). Artificial Intelligence in Medicine 2024;151:102841 View
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Books/Policy Documents

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