Despite the silent effects sometimes hidden to the major audience, air pollution is becoming one of the most impactful threat to global health. Cities are the places where deaths due to air pollution are concentrated most. In order to correctly address intervention and prevention thus is essential to assest the risk and the impacts of air pollution spatially and temporally inside the urban spaces. PULSE aims to design and build a large-scale data management system enabling real time analytics of health, behaviour and environmental data on air quality. The objective is to reduce the environmental and behavioral risk of chronic disease incidence to allow timely and evidence-driven management of epidemiological episodes linked in particular to two pathologies; asthma and type 2 diabetes in adult populations. developing a policy-making across the domains of health, environment, transport, planning in the PULSE test bed cities.

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.

Informatics for Integrating Biology and the Bedside (i2b2) is an initiative funded by the NIH that aims at building an informatics infrastructure to support biomedical research. The University of Pavia has recently integrated i2b2 infrastructure with a registry of inherited arrhythmogenic diseases. Within this project, the authors created a novel i2b2 cell, named R Engine Cell, which allows the communication between i2b2 and the R statistical software. As survival analyses are routinely performed by cardiology researchers, the authors have first concentrated on making Kaplan-Meier analyses available within the i2b2 web interface. To this aim, the authors developed a web-client plug-in to select the patient set on which to perform the analysis and to display the results in a graphical, intuitive way. R Engine Cell has been designed to easily support the integration of other R-based statistical analyses into i2b2.

In this paper we introduce a novel method to extract from data and graphically represent the temporal relationships between events, called precedence temporal network. The new approach first derives events from time series by exploiting the temporal abstraction technique, then derives temporal precedence between abstractions in terms of association rules and finally expresses the relationships as a labeled graph. The method is applied to the problem of representing the temporal behavior of gene expressions, as they are collected by DNA microarrays. In particular, in this paper we present the results obtained from the analysis of the expression of a subset of the genes involved in cell-cycle regulation.

In this article we present a new approach for the intelligent analysis of longitudinal data coming from chronic patients home monitoring. This approach exploits temporal abstractions to pre-process the raw data and to obtain a new time series of abstract episodes, whose features are then interpreted through statistical and probabilistic techniques. We describe in detail an application of the presented technique to the analysis of diabetic patients' data, showing some results obtained on a real case monitored for six months.

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, nonparametric 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.