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

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;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
  22. Banoei M, Rafiepoor H, Zendehdel K, Seyyedsalehi M, Nahvijou A, Allameh F, Amanpour S. Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study. Frontiers in Medicine 2023;10 View
  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

Books/Policy Documents

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