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University of Pavia, Italy

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24 Papers

Learning T2D Evolving Complexity from EMR and Administrative Data by Means of Continuous Time Bayesian Networks

Topics: Datamining; Electronic Health Records and Standards; Pattern Recognition and Machine Learning

Authors: Simone Marini , Arianna Dagliati , Lucia Sacchi and Riccardo Bellazzi

Affiliation: University of Pavia, Italy

Digital Object Identifier: 10.5220/0005708103380344

Proceedings of HEALTHINF 2016/BIOSTEC, ISBN: 978-989-758-170-0

Quick Abstract

Predicting the complexity level (i.e. the number of complications and their related hospitalizations) in a T2D cohort is a critical step in prevention, resource optimization and overall patient management. Our data set was obtained by monitoring a T2D diabetic cohort along up to 10 years through electronic medical records of a local healthcare agency data warehouse. In order to conveniently handle temporarily sparse data, we designed a model describing the cohort evolution with Continuous Time Bayesian Networks (CTBN). The network structure and its parameters are entirely data driven. Compared to traditional Bayesian Networks, CTBNs admit cycles. As consequence, CTBNs fit the complexity of chronic metabolic syndromes where variables show a reciprocal influence. Network nodes represent metabolic (glycated hemoglobin, lipid profile (cholesterol, triglycerides), and biometric (BMI) data. We observed how these variables directly or indirectly affect the disease level of complexity, and how the variables influence the cumulative adverse events a patient undergoes.

Related Ontology Subjects/Areas/Topics (14)

Artificial Intelligence	Databases and Information Systems Integration	Pattern Recognition and Machine Learning
Biomedical Engineering	Datamining	Sensor Networks
Business Analytics	Electronic Health Records and Standards	Signal Processing
Data Engineering	Enterprise Information Systems	Soft Computing
Data Mining	Health Information Systems

Exposome, Health and Biomedical Informatics - An Emerging Discipline and Its Interaction with Current Biomedical Informatics

Topics: Medical and Nursing Informatics; Wearable Health Informatics; Mobile Technologies for Healthcare Applications; e-Health; e-Health for Public Health

Authors: Guillermo Lopez-Campos ¹ , Riccardo Bellazzi ² and Fernando Martin-Sanchez ¹

Affiliations: ¹ The University of Melbourne, Australia , ² University of Pavia, Italy

Digital Object Identifier: 10.5220/0005278405800584

Proceedings of HEALTHINF 2015/BIOSTEC, ISBN: 978-989-758-068-0

Quick Abstract

In the last decade we have witnessed the raising of the exposome (the set of a life-long individual exposures) as an increasingly interesting area and discipline due to its relationship with health. These new approaches rely heavily in the use of different informatics related methods and are generating new data types that in the future should be handled by biomedical informatics. This position paper refers to some of the challenges that are related with these new approaches from a biomedical informatics perspective, describing the interactions with related disciplines such as bioinformatics, public health informatics and others. We discuss as well the role of the exposome in bringing new data types that might be handled by biomedical informatics in the context of Big and small data generated in this approaches and its relationship with the participatory medicine and how they could influence future health information systems. Finally, we consider that the current situation of the exposome resembles the early years of genomics, when it was clear that genomic information had a great potential for health and drove a discussion about how to better integrate and analyse the most relevant pieces of information for health purposes.

Related Ontology Subjects/Areas/Topics (21)

Biomedical Engineering	e-Health	Mobile Technologies for Healthcare Applications
Biomedical Signal Processing	e-Health for Public Health	Neural Rehabilitation
Cardiovascular Technologies	Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics
Cloud Computing	Health Information Systems	Physiological Computing Systems
Computing and Telecommunications in Cardiology	Human-Computer Interaction	Platforms and Applications
Devices	Medical and Nursing Informatics	Software Engineering
Distributed and Mobile Software Systems	Mobile Technologies	Wearable Sensors and Systems

A New Algorithm for Objective Video Quality Assessment on Eye Tracking Data

Topic: Visual Attention and Image Saliency

Authors: Maria Grazia Albanesi and Riccardo Amadeo

Affiliation: University of Pavia, Italy

Digital Object Identifier: 10.5220/0004672104620469

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-003-1

Quick Abstract

In this paper, we present an innovative algorithm based on a voting process approach, to analyse the data provided by an eye tracker during tasks of user evaluation of video quality. The algorithm relies on the hypothesis that a lower quality video is more “challenging” for the Human Visual System (HVS) than a high quality one, and therefore visual impairments influence the user viewing strategy. The goal is to generate a map of saliency of the human gaze on video signals, in order to create a No Reference objective video quality assessment metric. We consider the impairment of video compression (H.264/AVC algorithm) to generate different versions of video quality. We propose a protocol that assigns different playlists to different user groups, in order to avoid any effect of memorization of the visual stimuli on strategy. We applied our algorithm to data generated on a heterogeneous set of video clips, and the final result is the computation of statistical measures which provide a rank of the videos according to the perceived quality. Experimental results show that there is a strong correlation between the metric we propose and the quality of impaired video, and this fact confirms the initial hypothesis.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Image and Video Analysis

Visual Attention and Image Saliency

In-chair Movements of Healthy People during Prolonged Sitting

Topics: Pattern Recognition; Observation, Modeling and Prediction of User Behavior; Pervasive Technologies; Assistive Technologies

Authors: Elisa Marenzi , Gian Mario Bertolotti and Giovanni Danese

Affiliation: University of Pavia, Italy

Digital Object Identifier: 10.5220/0004723101450152

Proceedings of PhyCS 2014, ISBN: 978-989-758-006-2

Quick Abstract

This paper describes a program designed to detect and give a classification of the in-chair movements done by healthy people while seated for long periods of time. The purpose of this work is to identify the frequency, duration and typology of movements performed by subjects that need to remain seated for a prolonged time. The software finds the time instants of each movement, its duration and whether it is in the sagittal or the lateral plane; in particular it distinguishes between a left and right movement (in the lateral plane) and a forward or backward trunk movement. This information can be useful in many different domains: first of all to monitor the fidgeting phenomenon and consequently the feeling of discomfort in the office environment; it can be adopted to evaluate the fatigue of car and truck drivers; but the most important outcome concerns the clinical setting, in which it can be very helpful for the medical staff in determining an appropriate and personalized rehabilitation strategy for patients with motor limitations in order to prevent the development of pressure ulcers.

Related Ontology Subjects/Areas/Topics (19)

Applications	Human-Computer Interaction	Pattern Recognition
Artificial Intelligence	Methodologies and Methods	Pervasive Technologies
Assistive Technologies	Neural Rehabilitation	Physiological Computing Systems
Biomedical Engineering	Neurocomputing	Sensor Networks
Biomedical Signal Processing	Neurotechnology, Electronics and Informatics	Soft Computing
Data Manipulation	Observation, Modeling and Prediction of User Behavior	Software Engineering
Health Engineering and Technology Applications

A MULTI-SENSOR SYSTEM FOR FALL DETECTION IN AMBIENT ASSISTED LIVING CONTEXTS

Topic: Home Monitoring and Assisted Living Applications

Authors: Giovanni Diraco ¹ , Alessandro Leone ¹ , Pietro Siciliano ¹ , Marco Grassi ² and Piero Malcovati ²

Affiliations: ¹ CNR, Italy , ² University of Pavia, Italy

Digital Object Identifier: 10.5220/0003834202130219

Proceedings of SENSORNETS 2012, ISBN: 978-989-8565-01-3

Quick Abstract

The aging population represents an emerging challenge for healthcare since elderly people frequently suffer from chronic diseases requiring continuous medical care and monitoring. Sensor networks are possible enabling technologies for ambient assisted living solutions helping elderly people to be independent and to feel more secure. This paper presents a multi-sensor system for the detection of people falls in home environment. Two kinds of sensors are used: a wearable wireless accelerometer with onboard fall detection algorithms and a time-of-flight camera. A coordinator node receives data from the two sub-sensory systems with their associated level of confidence and, on the basis of a data fusion logic, it operates the validation and correlation among the two sub-systems delivered data in order to rise overall system performance with respect to each single sensor sub-system. Achieved results show the effectiveness of the suggested multi-sensor approach for improving fall detection service in ambient assisted living contexts.

Related Ontology Subjects/Areas/Topics (3)

Applications and Uses

Home Monitoring and Assisted Living Applications

Sensor Networks

GPU-based Parallel Implementation of a Growing Self-organizing Network

Authors: Giacomo Parigi ¹ , Angelo Stramieri ¹ , Danilo Pau ² and Marco Piastra ¹

Affiliations: ¹ University of Pavia, Italy , ² STMicroelectronics, Italy

Digital Object Identifier: 10.5220/0004133806330643

Proceedings of ANNIIP 2012/ICINCO, ISBN: 978-989-8565-21-1

Quick Abstract

Self-organizing systems are characterized by an inherently local behavior, as their configuration is almost exclusively determined by the union of the states of each of the units composing the system. Moreover, all state changes are mutually independent and governed by the same laws. In this work we study the parallel implementation of a specific subset of this broader family, namely that of growing self-organizing networks, in relation to parallel computing hardware devices based on Graphic Processing Units (GPUs), which are increasingly gaining popularity due to their favourable cost/performance ratio. In order to do so, we first define a new version of the standard, sequential algorithm, where the intrinsic parallelism of the execution is made more explicit and then we perform comparative experiments with the standard algorithm, together with an optimized variant of the latter, where an hash index is used for speed. Our experiments demonstrates that the parallel version outperforms both variants of the sequential algorithm but also reveals a few interesting differences in the overall behavior of the system, that might be relevant for further investigations.

FAST REAL-TIME SEGMENTATION AND TRACKING OF MULTIPLE SUBJECTS BY TIME-OF-FLIGHT CAMERA - A New Approach for Real-time Multimedia Applications with 3D Camera Sensor

Topics: Motion and Tracking; Segment Cluster Tracking

Authors: Piercarlo Dondi and Luca Lombardi

Affiliation: University of Pavia, Italy

Digital Object Identifier: 10.5220/0003322505820587

Proceedings of VISAPP 2011/VISIGRAPP, ISBN: 978-989-8425-47-8

Quick Abstract

Time-of-Flight cameras are a new kind of sensors that use near-infrared light to provide distance measures of an environment. In this paper we present a very fast method for real-time segmentation and tracking, that exploits the peculiar characteristics of these devices. The foreground segmentation is achieved by a dynamic thresholding and region growing: an appropriate correction based on flexible intensity thresholding and mathematical morphology is used to partially compensate one of the most common problem of the TOF cameras, the noise generated by sun light. By the use of a Kalman filter for tracking the retrieved objects the system is able to correctly handle the occlusions and to follow multiple objects placed at different distances. The proposed system is our basic step for complex multimedia applications, such as augmented reality. An example of mixed reality that includes the integration of color information, supplied by a webcam is shown in the experimental results.

Related Ontology Subjects/Areas/Topics (9)

Applications	Methodologies and Methods	Pattern Recognition
Computer Vision, Visualization and Computer Graphics	Motion and Tracking	Physiological Computing Systems
Human-Computer Interaction	Motion, Tracking and Stereo Vision	Segment Cluster Tracking

GEOMETRICAL CONSTRAINTS FOR LIGAND POSITIONING

Topics: Genomics and Proteomics; Pattern Recognition, Clustering and Classification

Authors: Virginio Cantoni , Alessandro Gaggia , Riccardo Gatti and Luca Lombardi

Affiliation: University of Pavia, Italy

Digital Object Identifier: 10.5220/0003166002040209

Proceedings of BIOINFORMATICS 2011/BIOSTEC, ISBN: 978-989-8425-36-2

Quick Abstract

The purpose of the activity here described is the morphological and subsequently the geometrical and topological analysis of the active sites in protein surfaces for protein-ligand docking. The approach follows a sequence of three steps: i) the solvent-excluded-surface is analyzed and segmented in a number of pockets and tunnels; ii) the candidate binding sites are detected through a structural matching of pockets and ligand, both represented through a suitable Extended Gaussian Image modality; iii) the loci of compatible positions of the ligand is identified through mathematical morphology. This representation of ligand and candidate binding pockets, the comparison of the morphological similarity and the identification of potential ligand docking are the novelties of this proposal.

Related Ontology Subjects/Areas/Topics (4)

Bioinformatics		Genomics and Proteomics		Pattern Recognition, Clustering and Classification
Biomedical Engineering

INFERENTIAL MINING FOR RECONSTRUCTION OF 3D CELL STRUCTURES IN ATOMIC FORCE MICROSCOPY IMAGING

Topics: Concept Mining; Pre-Processing and Post-Processing for Data Mining; Visual Data Mining and Data Visualization

Authors: Mario D'Acunto ¹ , Stefano Berrettini ² , Serena Danti ² , Michele Lisanti ² , Mario Petrini ² , Andrea Pietrabissa ³ and Ovidio Salvetti ¹

Affiliations: ¹ ISTI-CNR, Italy , ² University of Pisa, Italy , ³ University of Pavia, Italy

Digital Object Identifier: 10.5220/0003685503480353

Proceedings of KDIR 2011/IC3K, ISBN: 978-989-8425-79-9

Quick Abstract

Atomic Force Microscopy (AFM) is a fundamental tool for the investigation of a wide range of mechanical properties on nanoscale due to the contact interaction between the AFM tip and the sample surface. The focus of this paper is on an algorithm for the reconstruction of 3D stem-differentiated cell structures extracted by typical 2D surface AFM images. The AFM images resolution is limited by the tip-sample convolution due to the combined geometry of the probe tip and the pattern configuration of the sample. This limited resolution limits the accuracy of the correspondent 3D image. To drop unwanted effects, we adopt an inferential method for pre-processing single frame AFM image (low resolution image) building its super-resolution version. Therefore the 3D reconstruction is made on animal cells using a Markov Random Field approach for augmented voxels. The 3D reconstruction should improve unambiguous identification of cells structures. The computation method is fast and can be applied both to multi- and to single-frame images.

Related Ontology Subjects/Areas/Topics (7)

Artificial Intelligence	Knowledge-Based Systems	Symbolic Systems
Concept Mining	Pre-Processing and Post-Processing for Data Mining	Visual Data Mining and Data Visualization
Knowledge Discovery and Information Retrieval

NONPARAMETRIC VIRTUAL SENSORS FOR SEMICONDUCTOR MANUFACTURING - Using Information Theoretic Learning and Kernel Machines

Topics: Systems Modeling and Simulation; Industrial Automation and Robotics

Authors: Andrea Schirru ¹ , Simone Pampuri ² , Cristina De Luca ³ and Giuseppe De Nicolao ¹

Affiliations: ¹ University of Pavia, Italy , ² University of Pavia and Infineon Technologies Austria, Italy , ³ Infineon Technologies Austria, Austria

Digital Object Identifier: 10.5220/0003520403490358

Proceedings of ICINCO 2011, ISBN: 978-989-8425-75-1

Quick Abstract

In this paper, a novel learning methodology is presented and discussed with reference to the application of virtual sensors in the semiconductor manufacturing environment. Density estimation techniques are used jointly with Renyi’s entropy to define a loss function for the learning problem (relying on Information Theoretic Learning concepts). Furthermore, Reproducing Kernel Hilbert Spaces (RKHS) theory is employed to handle nonlinearities and include regularization capabilities in the model. The proposed algorithm allows to estimate the structure of the predictive model, as well as the associated probabilistic uncertainty, in a nonparametric fashion. The methodology is then validated using simulation studies and process data from the semiconductor manufacturing industry. The proposed approach proves to be especially effective in strongly nongaussian environments and presents notable outlier filtering capabilities.

Related Ontology Subjects/Areas/Topics (4)

Industrial Automation and Robotics		Informatics in Control, Automation and Robotics		Systems Modeling and Simulation
Industrial Engineering

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