ArticleJournal of personalized medicine2021
Predicting in-Hospital Mortality of Patients with COVID-19 Using Machine Learning Techniques.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review.Frontiers in public health · 2023Pooled it
- Effects of environment and globalization on the double and triple burdens of infection symptoms among under-five children across low-middle income countries using machine learning algorithms.Infectious diseases of poverty · 2025Article
- Development of decision tree classification algorithms in predicting mortality of COVID-19 patients.International journal of emergency medicine · 2024Article
- Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.BMC public health · 2024Article
- Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset.BMC medical informatics and decision making · 2024Article
- Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape.Critical care clinics · 2023Review
- Machine learning-based mortality prediction models for smoker COVID-19 patients.BMC medical informatics and decision making · 2023Article
- Selected Predictors of COVID-19 Mortality in the Hospitalised Patient Population in a Single-Centre Study in Poland.Healthcare (Basel, Switzerland) · 2023Article
- Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa.Frontiers in artificial intelligence · 2023Article
- Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19.Diagnostics (Basel, Switzerland) · 2022Article
- Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States.Healthcare (Basel, Switzerland) · 2022Article
- Prediction of COVID-19 Patients' Survival by Deep Learning Approaches.Medical journal of the Islamic Republic of Iran · 2022Article
- Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity.BioMed research international · 2022Article
- Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury.Journal of personalized medicine · 2021Article
- COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal.Journal of personalized medicine · 2021Review
- COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm.Journal of anesthesia, analgesia and critical care · 2021Article
- Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
- Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients.International journal of environmental research and public health · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present work aims to identify the predictors of COVID-19 in-hospital mortality testing a set of Machine Learning Techniques (MLTs), comparing their ability to predict the outcome of interest. The model with the best performance will be used to identify in-hospital mortality predictors and to build an in-hospital mortality prediction tool. The study involved patients with COVID-19, proved by PCR test, admitted to the "Ospedali Riuniti Padova Sud" COVID-19 referral center in the Veneto region, Italy. The algorithms considered were the Recursive Partition Tree (RPART), the Support Vector Machine (SVM), the Gradient Boosting Machine (GBM), and Random Forest. The resampled performances were reported for each MLT, considering the sensitivity, specificity, and the Receiving Operative Characteristic (ROC) curve measures. The study enrolled 341 patients. The median age was 74 years, and the male gender was the most prevalent. The Random Forest algorithm outperformed the other MLTs in predicting in-hospital mortality, with a ROC of 0.84 (95% C.I. 0.78-0.9). Age, together with vital signs (oxygen saturation and the quick SOFA) and lab parameters (creatinine, AST, lymphocytes, platelets, and hemoglobin), were found to be the strongest predictors of in-hospital mortality. The present work provides insights for the prediction of in-hospital mortality of COVID-19 patients using a machine-learning algorithm.
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