1,721,028 research outputs found
Spatio-temporal resource mapping for intensive care units at regional level for COVID-19 emergency in Italy
No full textCOVID-19 is a worldwide emergency since it has rapidly spread from China to almost all the countries worldwide. Italy has been one of the most affected countries after China. North Italian regions, such as Lombardia and Veneto, had an abnormally large number of cases. COVID-19 patients management requires availability of sufficiently large number of Intensive Care Units (ICUs) beds. Resources shortening is a critical issue when the number of COVID-19 severe cases are higher than the available resources. This is also the case at a regional scale. We analysed Italian data at regional level with the aim to: (i) support health and government decision-makers in gathering rapid and efficient decisions on increasing health structures capacities (in terms of ICU slots) and (ii) define a geographic model to plan emergency and future COVID-19 patients management using reallocating them among health structures. Finally, we retain that the here proposed model can be also used in other countries
Using dual-network-analyser for communities detecting in dual networks
No full textBackground: Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks (DNs) consist of mapping information as pairs of networks containing the same set of nodes but with different edges: one, called physical network, has unweighted edges, while the other, called conceptual network, has weighted edges. Results: We focus on DNs and we propose a tool to find common subgraphs (aka communities) in DNs with particular properties. The tool, called Dual-Network-Analyser, is based on the identification of communities that induce optimal modular subgraphs in the conceptual network and connected subgraphs in the physical one. It includes the Louvain algorithm applied to the considered case. The Dual-Network-Analyser can be used to study DNs, to find common modular communities. We report results on using the tool to identify communities on synthetic DNs as well as real cases in social networks and biological data. Conclusion: The proposed method has been tested by using synthetic and biological networks. Results demonstrate that it is well able to detect meaningful information from DNs
A Novel Algorithm for Local Network Alignment Based on Network Embedding
No full textNetworks are widely used in bioinformatics and biomedicine to represent associations across a large class of biological entities. Network alignment refers to the set of approaches that aim to reveal similarities among networks. Local Network Alignment (LNA) algorithms find (relatively small) local regions of similarity between two or more networks. Such algorithms are in general based on a set of seed nodes that are used to build the alignment incrementally. A large fraction of LNA algorithms uses a set of vertices based on context information as seed nodes, even if this may cause a bias or a data-circularity problem. Moreover, using topology information to choose seed nodes improves overall alignment. Finally, similarities among nodes can be identified by network embedding methods (or representation learning). Given there are two networks, we propose to use network embedding to capture structural similarity among nodes, which can also be used to improve LNA effectiveness. We present an algorithm and experimental tests on real and syntactic graph data to find LNAs
Extracting Dense and Connected Communities in Dual Networks: An Alignment Based Algorithm
No full textNetworks-based models have been used to represent and analyse datasets in many fields such as
computational biology, medical informatics and social networks. Nevertheless, it has been recently shown
that, in their standard form, they are unable to capture some aspects of the investigated scenarios. Thus,
more complex and enriched models, such as heterogeneous networks or dual networks, have been proposed.
We focus on the latter model, which consists of a pair of networks having the same nodes but different
edges. In dual networks, one network, called physical, has unweighted edges representing binary associations
among nodes. The other is an edge-weighted one where weights represent the strength of the associations
among nodes. Dual networks capture in a single model some aspects that cannot be described by using a
standard model. Dual networks can be used, for instance, to capture a co-authorships network, where physical
network represents co-authors. In contrast, the conceptual network is used to model topics sharing among
a couple of authors by means of edge connections. This allows capturing similar interests among authors
even though they are not co-authors. We propose an innovative algorithm to find the Densest Connected
Subgraph (DCS) in dual networks. DCS is the largest density subgraph in the conceptual network, which is
also connected in the physical network. A DCS represents a set of highly similar nodes. Moreover, since DCS
is a computationally hard problem, we propose novel heuristics to solve it. We tested the proposed algorithm
on social, biological, and co-authorship networks. Results demonstrate that our approach is efficient and is
able to extract meaningful information from dual networks
A tool for the semiautomatic acquisition of the morphological data of blood vessel networks
No full textDesign and Implementation of a Telecardiology System for Mobile Devices
No full textThis paper presents the design and implementation of a system for digital telecardiology on mobile devices called Remote Cardio Consultation (RCC). Using RCC may improve first intervention procedures in case of heart attack. In fact, it allows physicians to remotely consult ECG signals from a mobile device or smartphone by using a so-called app. The remote consultation is implemented by a server application collecting physician availability to answer upon client support requests. The app can be used by first intervention clinicians and allows reducing delays and decision errors in emergency interventions. Thus, best decision, certified and supported by cardiologists, can be obtained in case of heart attacks and first interventions even by base medical doctors able to produce and send an ECG. RCC tests have been performed, and the prototype is freely available as a service for testing
An information system to track data and processes for food quality and bacterial pathologies prevention
No full textOn the Use of GIS for Health and Epidemiology Control
No full textMonitoring the evolution and diffusion of diseases is an important task for health monitoring. Recent phenomena, such as the
spread of Coronavirus (Covid-19), highlighted the relevance of adopting Geographical Information Systems (GIS) in epidemiology modeling.
GIS may offer general views and indications which domain experts and
governments could use to give immediate directions to social actors and
operators.
We report on the possibility of using geographic database model instruments in order to acquire, store and manage health-related data.
The reported case study is about an application able to correlate TSH
(Thyroid-Stimulating Hormone) with environmental data. The reported
example aims to show how to acquire, analyze and integrate clinical and
geographical data to evaluate possible correlations for the prevention
of chronic diseases, especially neoplasms, by means of mapping disease
features with environmental factors
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