ArticleJournal of medical Internet research2021
A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- Effects of the pre-existing coronary heart disease on the prognosis of COVID-19 patients: A systematic review and meta-analysis.PloS one · 2023Pooled it
- Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review.Frontiers in public health · 2023Pooled it
- Unveiling etiology and mortality risks in community-acquired pneumonia: A machine learning approach.Biomolecules & biomedicine · 2025Article
- Surfactant-derived protein type B: a new biomarker linked to respiratory failure and lung damage in mild to moderate SARS-CoV-2 pneumonia.ERJ open research · 2024Article
- Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model.Diagnostics (Basel, Switzerland) · 2024Article
- Prognostic value of respiratory parameters for COVID-19 patients in the emergency department: results from the EC-COVID study.Internal and emergency medicine · 2023Article
- Machine learning-based mortality prediction models for smoker COVID-19 patients.BMC medical informatics and decision making · 2023Article
- 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 · 2023Article
- Predicting the mortality of patients with Covid-19: A machine learning approach.Health science reports · 2023Article
- Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers.Biomedicines · 2023Article
- 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 · 2023Article
- A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study.Scientific reports · 2022Article
- Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning.Diagnostics (Basel, Switzerland) · 2022Article
- External validation of the 4C Mortality Score for hospitalised patients with COVID-19 in the RECOVER network.BMJ open · 2022Article
- 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 · 2022Article
- An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study.Archives of medical science : AMS · 2022Article
- Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients.PloS one · 2022Article
- Article
- Demystifying machine learning for mortality prediction.Critical care (London, England) · 2021Article
- Artificial Intelligence in Digital Pathology: What Is the Future?Healthcare (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
20 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling.
objectiveWe aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia.
methodsThe study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO
resultsThe Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively.
conclusionsOur findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.