@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 {,
	title = {An agent-based WCET analysis for Top-View Person Re-Identification},
	booktitle = {1st International Workshop on Real-Time Compliant Multi-Agent Systems (RTcMAS)},
	year = {2018},
	publisher = {CEUR Workshop Proceedings},
	organization = {CEUR Workshop Proceedings},
	address = {Stockholm},
	abstract = {<p>Person re-identification is a challenging task for improving and personalising the shopping experience in an intelligent retail environment. A new Top View Person Re-Identification (TVPR) dataset of 100 persons has been collected and described in a previous work. This work estimates the Worst Case Execution Time (WCET) for the features extraction and classification steps. Such tasks should not exceed the WCET, in order to ensure the effectiveness of the proposed application. In fact, after the features extraction, the classification process is performed by selecting the first passage under the camera for training and using the others as the testing set. Furthermore, a gender classification is exploited for improving retail applications. We tested all feature sets using k-Nearest Neighbors, Support Vector Machine, Decision Tree and Random Forest classifiers. Experimental results prove the effectiveness of the proposed approach, achieving good performance in terms of Precision, Recall and F1-score.</p>
},
	keywords = {Person re-identification, Real-time, Retail, RGB-D camera, WCET},
	url = {http://ceur-ws.org/Vol-2156/paper4.pdf},
	author = {Marina Paolanti and Valerio Placidi and Michele Bernardini and Andrea Felicetti and Rocco Pietrini 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}
}
