@article {,
	title = {Discovering the Type 2 Diabetes in Electronic Health Records using the Sparse Balanced Support Vector Machine},
	journal = {IEEE Journal of Biomedical and Health Informatics },
	year = {2019},
	abstract = {<p>The diagnosis of Type 2 Diabetes (T2D) at an early stage has a key role for an adequate T2D integrated management system and patient{\textquoteright}s follow-up. Recent years have witnessed an increasing amount of available Electronic Health Record (EHR) data and Machine Learning (ML) techniques have been considerably evolving. However, managing and modeling this amount of information may lead to several challenges such as overfitting, model interpretability and computational cost. Starting from these motivations, we introduced a ML method called Sparse Balanced Support Vector Machine (SB-SVM) for discovering T2D in a novel collected EHR dataset (named FIMMG dataset). In particular, among all the EHR features related to exemptions, examination and drug prescriptions we have selected only those collected before T2D diagnosis from a uniform age group of subjects. We demonstrated the reliability of the introduced approach with respect to other ML and Deep Learning approaches widely employed in the state-of-the-art for solving this task. Results evidence that the SB-SVM overcomes the other state-of-the-art competitors providing the best compromise between predictive performance and computation time. Additionally, the induced sparsity allows to increase the model interpretability, while implicitly managing high dimensional data and the usual unbalanced class distribution.</p>
},
	keywords = {Decision Support System, Electronic Health Record, Machine Learning, Support Vector Machine, Type 2 Diabetes},
	issn = {2168-2208},
	doi = {10.1109/JBHI.2019.2899218},
	url = {https://ieeexplore.ieee.org/abstract/document/8641396},
	author = {Michele Bernardini and Luca Romeo and Paolo Misericordia and Emanuele Frontoni}
}
@article {Paolanti2018,
	title = {Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection},
	journal = {Sensors},
	volume = {18},
	number = {10},
	year = {2018},
	month = {oct},
	pages = {3471},
	publisher = {{MDPI} {AG}},
	doi = {10.3390/s18103471},
	url = {https://doi.org/10.3390/s18103471},
	author = {Marina Paolanti and Luca Romeo and Daniele Liciotti and Annalisa Cenci and Emanuele Frontoni and Primo Zingaretti}
}
