Dynamic Assessment of Personal Exposure to Air Pollution for Everyone: a Smartphone-Based Approach

Abstract. In Epidemiology, exposure assessment is the process of measuring or estimating the intensity of human exposures to an environmental agent such as air pollution. Healthcare agencies typically take into consideration yearly averaged pollution values and apply them to all citizens, in risk models. However distinct parts of cities can have significantly different levels of pollution and individual habits can influence exposure, too. Consequently, in epidemiology and public health, there is an increasing interest for personal exposure assessment, i.e. the capability of measuring the exposure of individuals. Within the EU H2020 PULSE project, an innovative mechanism for the individual and dynamic assessment of exposure to air pollution has been implemented. The present paper illustrates its technological and scientific components. The system has already been deployed to several pilot cities of the project and Pavia, Italy, has been the first one. In that city several hundreds of tracks have already been acquired and processed. Therefore, the paper thoroughly illustrates the assessment procedure with examples

A semantic collaborative system for the management of translational research projects

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i2b2 to Optimize Patients Enrollment.

i2b2 data-warehouse could be a useful tool to support the enrollment phase of clinical studies. The aim of this work is to evaluate its performance on two clinical trials. We developed also an i2b2 extension to help in suggesting eligible patients for a study. The work showed good results in terms of ability to implement inclusion/exclusion criteria, but also in terms of identified patients actually enrolled and high number of patients suggested as potentially enrollable

Effects of an online supervised exercise training in children with obesity during the COVID-19 pandemic

COVID-19 restrictions have dramatically reduced the active lifestyle and physical activity (PA) levels in the whole population, a situation that can contribute to weight gain and to develop obesity. To improve physical fitness (PF) in children with obesity during COVID-19 restrictions, sport specialists started to deliver physical training through tele-exercise. For these reasons, the aim of this study was to evaluate the effects of a 12-week online supervised training program in children with obesity on different PF components and PA levels. We enrolled a total of 40 Caucasian children (9 F/31 M; aged 11 +/- 1.9 years) with obesity. The data collection consisted of a series of anthropometric measures, the PAQ-C questionnaire, and PF tests, valid and reliable tools to assess PF in children. We used a Wilcoxon's t-test and a Student's t-test, as appropriate, to assess the differences before and after the training protocol. A total of 37 patients completed the training protocol and were considered in the analysis. Our results show an improvement in all the PF tests, a reduction in the BMI z-score, the waist circumference, and in the waist-to-height ratio, and an increased PA level. In conclusion, the results of our study show that an online supervised training program is effective to promote PA, improving PF and reducing the BMI z-score in children with obesity

Transfer Learning for Urban Landscape Clustering and Correlation with Health Indexes

Within the EU-funded Pulse project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches to jointly analyze maps and geospatial information with health care data and air pollution measurements. In this paper we describe a component of such platform, designed to couple deep learning analysis of geospatial images of cities and some healthcare and behavioral indexes collected by the 500 cities US project, showing that, in New York City, urban landscape significantly correlates with the access to healthcare services

Extraction of Clinical Information from Clinical Reports: an Application to the Study of Medication Overuse Headaches in Italy.

International audienceA i2b2-Pavia pilot project has been recently activated at the Headache Centre of the C. Mondino Institute of Neurology, in Pavia, with the aim of investigating Medical Overuse Headaches. The software infrastructure so far implemented automatically extracts and integrates data coming from different sources into a repository purposely designed for multidimensional inspection. A great effort has been devoted to train a Natural Language Processing system able to extract medical concepts from Italian clinical reports

A unified approach for modeling longitudinal and failure time data, with application in medical monitoring

This paper considers biomedical problems in which a sample of subjects, for example clinical patients, is monitored through time for purposes of individual prediction. Emphasis is on situations in which the monitoring generates data both in the form of a time series and in the form of events (development of a disease, death, etc.) observed on each subject over specified intervals of time. A Bayesian approach to the combined modeling of both types of data for purposes of prediction is presented. The proposed method merges ideas of Bayesian hierarchical modeling, non parametric smoothing of time series data, survival analysis, and forecasting into a unified framework. Emphasis is on flexible modeling of the time series data based on stochastic process theory. The use of Markov Chain Monte Carlo simulation to calculate the predictions of interest is discussed. Conditional independence graphs are used throughout for a clear presentation of the models. An application in the monitoring of transplant patients is presented

A Framework for Temporal Data Processing and Abstractions

This paper presents Tempo, a framework for the definition, generation and execution of data processing components. Its architecture is organized on pipelines of modules assembled according to a specific meta-model with respect of contract based communication rules. Each pipeline wraps one or more data processing algorithms provided as reusable blocks in the default package. Such package can be extended with custom solutions through a plug-in mechanism. The Tempo components can be delivered both as web-services and as software library, and can be reused in different contexts by configuration through set of parameters. Although it has been initially tested in the medical field, Tempo is conceived as a general purpose framework. Until now, it has been integrated and tested within a medical guidelines implementation software tool and in a general purpose web application prototype as embedded module for the extraction of temporal patterns from generic time series