ArticleScientific reports2022
A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- CT-based AI system for quantitative and integrated management of acute respiratory distress syndrome in critical care.NPJ digital medicine · 2026Article
- Prediction of Lithium Therapeutic Target Attainment in Adolescents Using an XGBoost Model: A Single-Center Retrospective Study.Drug design, development and therapy · 2026Article
- COVID-19 severity analysis for clinical decision support based on machine learning approach.Scientific reports · 2025Article
- Development and validation of an interpretable nomogram for predicting the risk of the prolonged postoperative length of stay for tuberculous spondylitis: a novel approach for risk stratification.BMC musculoskeletal disorders · 2025Article
- Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties.Radiological physics and technology · 2025Article
- Predicting outcomes using neural networks in the intensive care unit.World journal of clinical cases · 2025Article
- Enhanced Skin Disease Classification via Dataset Refinement and Attention-Based Vision Approach.Bioengineering (Basel, Switzerland) · 2025Article
- A deep learning pipeline for morphological and viability assessment of 3D cancer cell spheroids.Biology methods & protocols · 2025Article
- Using machine learning methods to investigate the impact of comorbidities and clinical indicators on the mortality rate of COVID-19.Frontiers in medical technology · 2025Article
- Body Surface Potential Mapping: A Perspective on High-Density Cutaneous Electrophysiology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Prediction models for COVID-19 disease outcomes.Emerging microbes & infections · 2024Article
- Multicenter comparative analysis of local and aggregated data training strategies in COVID-19 outcome prediction with Machine learning.PLOS digital health · 2024Article
- Corticosteroid treatment prediction using chest X-ray and clinical data.Computational and structural biotechnology journal · 2024Article
- Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida.Diagnostics (Basel, Switzerland) · 2024Article
- Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions.Scientific reports · 2024Article
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
- Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A Deep Learning-Based Approach for Prediction of Vancomycin Treatment Monitoring: Retrospective Study Among Patients With Critical Illness.JMIR formative research · 2024Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for selection in a Boruta algorithm with SHAP game theoretical values. A classifier is built on the reduced feature space using CatBoost gradient boosting algorithm and reaching a probabilistic AUC of 0.949 on holdout test set. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.
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