FIMMG-COVID Dataset

The FIMMG_COVID dataset originated from a subset of the FIMMG EHR database in use at FIMMG Netmedica cloud architecture. The FIMMG_COVID dataset consists of 17147 patients collected from 11 different GPs’ EHR data. The mean and standard deviation of the age of each GP’s patient group were also reported. The 11 GPs (Core Data Team) were selected from those enrolled in the NMI cloud platform and they actively participated in defining the priority criteria. The Core Data Team is located in the North (4 GPs), the Center (4 GPs) and the South (3 GPs) of Italy and they assisted all the patients considered in the study. Additionally, among the hundreds of GPs enrolled in the NMI platform, these 11 selected are intensively involved in improving all the NMI cloud platform services. The Ethical Committees of University approved the experimental study and its guidelines as a clinical noninterventional (observational) study. FIMMG_COVID dataset is anonymous and their use, detention and conservation are regulated by an agreement between the FIMMG, NMI and University.

The FIMMG_COVID dataset has been utilised to develop the work:

Luca Romeo, Emanuele Frontoni, A Unified Hierarchical XGBoost Model for Classifying Priorities for COVID-19 Vaccination CampaignPattern Recognition,2021,108197,ISSN 0031-3203,https://doi.org/10.1016/j.patcog.2021.108197.

The code to replicate our experimental procedure is available at https://github.com/whylearning22/HPC-XGB

Please send an email to vrai@dii.univpm.it (put in cc the author l.romeo@univpm.it) for requesting the FIMMG_COVID dataset.

If you find our dataset helpful in your resarch or work, please cite our paper.

@article{ROMEO2021108197, title = {A Unified Hierarchical XGBoost Model for Classifying Priorities for COVID-19 Vaccination Campaign}, journal = {Pattern Recognition}, pages = {108197}, year = {2021}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2021.108197}, url = {https://www.sciencedirect.com/science/article/pii/S0031320321003794}, author = {Luca Romeo and Emanuele Frontoni}, }