@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}
}
@conference {innocenti2016development,
	title = {Development of an automatic procedure to mechanically characterize soft tissue materials},
	booktitle = {Mechatronic and Embedded Systems and Applications (MESA), 2016 12th IEEE/ASME International Conference on},
	year = {2016},
	pages = {1{\textendash}6},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Innocenti, Bernardo and Lambert, Pierre and Larrieu, Jean-Charles and Pianigiani, Silvia and Marina Paolanti and Michele Bernardini and Annalisa Cenci and Emanuele Frontoni}
}
@conference {Luchetti20131199,
	title = {Design and test of a precise mobile GPS tracker},
	booktitle = {2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings},
	year = {2013},
	note = {cited By 2},
	pages = {1199-1207},
	abstract = {In the last years the widespread diffusion of smartphones with sensing capabilities paved the way to smart pervasive applications. The tracking of users activities aided by the set of sensor installed on board of smartphones represents a really interesting market for users which today demand reliable, smart and tailored services. The target group of sportsmen was of particular concern in this study. We aim at offering them a tool to record their training sessions along with a web community to share their activities with similar users. To achieve this we have to deal with different issues concerning localization and modelling of the training session by using the GPS, as well as synchronization with the web service to upload the training data. {\textcopyright} 2013 IEEE.},
	doi = {10.1109/MED.2013.6608872},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84885209325\&partnerID=40\&md5=cd710f4031bf159dc90b4ee6ada8a129},
	author = {Luchetti, G. and Servici, G. and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
