@article {,
	title = {A Decision Support System for Diabetes Chronic Care Models based on General Practitioner engagement and EHR data sharing},
	journal = {IEEE Journal of Translational Engineering in Health and Medicine},
	year = {2020},
	doi = {10.1109/JTEHM.2020.3031107},
	url = {https://ieeexplore.ieee.org/document/9223696},
	author = {E. Frontoni and L. Romeo and M. Bernardini and S. Moccia and L. Migliorelli and M. Paolanti and A. Ferri and P. Misericordia and A. Mancini and P. Zingaretti}
}
@article {,
	title = {A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data Sharing},
	journal = {IEEE Journal of Translational Engineering in Health and Medicine},
	volume = {8},
	year = {2020},
	pages = {1-12},
	doi = {10.1109/JTEHM.2020.3031107},
	author = {E. Frontoni and L. Romeo and M. Bernardini and S. Moccia and L. Migliorelli and M. Paolanti and A. Ferri and P. Misericordia and A. Mancini and P. Zingaretti}
}
@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}
}
@article {Baldassarri2007367,
	title = {Detecting anomalous traffic using statistical discriminator and neural decisional motor},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {4527 LNCS},
	number = {PART 1},
	year = {2007},
	note = {cited By 0},
	pages = {367-376},
	abstract = {One of the main challenges in the information security concerns the introduction of systems able to identify intrusions. In this ambit this work takes place describing a new Intrusion Detection System based on anomaly approach. We realized a system with a hybrid solution between host-based and network-based approaches, and it consisted of two subsystems: a statistical system and a neural one. The features extracted from the network traffic belong only to the IP Header and their trend allows us detecting through a simple visual inspection if an attack occurred. Really the two-tier neural system has to indicate the status of the system. It classifies the traffic of the monitored host, distinguishing the background traffic from the anomalous one. Besides, a very important aspect is that the system is able to classify different instances of the same attack in the same class, establishing which attack occurs. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-38149012228\&partnerID=40\&md5=a6b1888f705a83520f222e21f9d88e66},
	author = {Paola Baldassarri and A. Montesanto and Paolo Puliti}
}
@conference {Baldassarri200774,
	title = {Detecting anomalous traffic using statistical processing and self-organizing maps},
	booktitle = {SECRYPT 2007 - International Conference on Security and Cryptography, Proceedings},
	year = {2007},
	note = {cited By 0},
	pages = {74-79},
	abstract = {The main idea of the present work is to create a system able to detect intrusions in computer networks. For this purpose we propose a novel intrusion detection system (IDS) based on an anomaly approach. We analyzed the network traffic from (outbound traffic) and towards (inbound traffic) a victim host through another host. Besides we realized an architecture consisted of two subsystems: a statistical subsystem and a neural networks based subsystem. The first elaborates chosen features extracted from the network traffic and it allows determining if an attack occurs through a preliminary visual inspection. The neural subsystem receives in input the output of the statistical subsystem and it has to indicate the status of the monitored host. It classifies the network traffic distinguishing the background traffic from the anomalous one. Moreover the system has to be able to classify different instances of the same attack in the same class, distinguishing in a completely autonomous way different typology of attack.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-67649764419\&partnerID=40\&md5=9a732b07f49d01bbb12964089390b414},
	author = {Paola Baldassarri and A. Montesanto and Paolo Puliti}
}
@conference {Tascini1991178,
	title = {Decision support system for capillaroscopic images},
	booktitle = {Proceedings of SPIE - The International Society for Optical Engineering},
	volume = {1450},
	year = {1991},
	note = {cited By 1},
	pages = {178-185},
	abstract = {The aim of the paper is to describe a decision support system operating in the area of capillaroscopic images. The system automatically sites the capillaroscopic analyzed image into one of the following classes: normal, diabetic and sclerodermic. The automatic morphometric analysis attempts to imitate physician behaviour and requires the introduction of some particular features connected with the specific domain. These features allow achieving a symbolic representation of the capillary partitioning it into three components: apex, arteriolar and venular side. The system is hierarchically organized in two levels. The system has been successfully used for obtaining images of nailfold capillaries of human finger.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0025798390\&partnerID=40\&md5=05b1aa9225d95f1ed2cecdc0a5268c2a},
	author = {Guido Tascini and Paolo Puliti and Primo Zingaretti}
}
