ArticlePattern recognition2022
A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign.
Article in Pattern recognition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 43 citations in OpenAlex.
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- Optimizing potato yield predictions in Uttar Pradesh, India: a comparative analysis of machine learning models.Scientific reports · 2025Article
- The moderating role of professional grief in the relationship between job stress dimensions and turnover intention among nurses.BMC psychology · 2025Article
- VirusPredictor: XGBoost-based software to predict virus-related sequences in human data.Bioinformatics (Oxford, England) · 2024Article
- COVID-19 and Rumors: A Dynamic Nested Optimal Control Model.Pattern recognition · 2023Article
- Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review.Journal of medical Internet research · 2023Article
- Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting.Frontiers in artificial intelligence · 2023Article
- Uncovering the potential of innovation ecosystems in the healthcare sector after the COVID-19 crisis.Health policy (Amsterdam, Netherlands) · 2023Article
- Identification and prediction of m7G-related Alzheimer's disease subtypes: insights from immune infiltration and machine learning models.Frontiers in aging neuroscience · 2023Article
- Multi-texture features and optimized DeepNet for COVID-19 detection using chest x-ray images.Concurrency and computation : practice & experience · 2022Article
- A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality.Epidemiology and infection · 2022Article
- Detection of COVID-19 using deep learning techniques and classification methods.Information processing & management · 2022Article
- Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh.PLOS global public health · 2022Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current ML approaches do not fully focus to answer a still unresolved and topical challenge, namely the prediction of priorities of COVID-19 vaccine administration. Thus, our task includes some additional methodological challenges mainly related to avoiding unwanted bias while handling categorical and ordinal data with a highly imbalanced nature. Hence, the main contribution of this study is to propose a machine learning algorithm, namely Hierarchical Priority Classification eXtreme Gradient Boosting for priority classification for COVID-19 vaccine administration using the Italian Federation of General Practitioners dataset that contains Electronic Health Record data of 17k patients. We measured the effectiveness of the proposed methodology for classifying all the priority classes while demonstrating a significant improvement with respect to the state of the art. The proposed ML approach, which is integrated into a clinical decision support system, is currently supporting General Pracitioners in assigning COVID-19 vaccine administration priorities to their assistants.
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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.