dc.contributor.author | Palleschi, Alessia | en_US |
dc.contributor.author | Petti, Manuela | en_US |
dc.contributor.author | Tieri, Paolo | en_US |
dc.contributor.author | Angelini, Marco | en_US |
dc.contributor.editor | Bernard, Jürgen | en_US |
dc.contributor.editor | Angelini, Marco | en_US |
dc.date.accessioned | 2022-06-02T14:59:50Z | |
dc.date.available | 2022-06-02T14:59:50Z | |
dc.date.issued | 2022 | |
dc.identifier.isbn | 978-3-03868-183-0 | |
dc.identifier.issn | 2664-4487 | |
dc.identifier.uri | https://doi.org/10.2312/eurova.20221075 | |
dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/eurova20221075 | |
dc.description.abstract | The traditional approach in medicine starts with investigating patients' symptoms to make a diagnosis. While with the advent of precision medicine, a diagnosis results from several factors that interact and need to be analyzed together. This added complexity asks for increased support for medical personnel in analyzing these data altogether. Our objective is to merge the traditional approach with network medicine to offer a tool to investigate together symptoms, anatomies, diseases, and genes to establish a diagnosis from different points of view. This paper aims to help the clinician with the typical workflow of disease analysis, proposing a Visual Analytics tool to ease this task. A use case demonstrates the benefits of the proposed solution. | en_US |
dc.publisher | The Eurographics Association | en_US |
dc.rights | Attribution 4.0 International License | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | CCS Concepts: Human-centered computing --> Visual analytics; Applied computing --> Computational genomics; Biological networks | |
dc.subject | Human centered computing | |
dc.subject | Visual analytics | |
dc.subject | Applied computing | |
dc.subject | Computational genomics | |
dc.subject | Biological networks | |
dc.title | Toward Disease Diagnosis Visual Support Bridging Classic and Precision Medicine | en_US |
dc.description.seriesinformation | EuroVis Workshop on Visual Analytics (EuroVA) | |
dc.description.sectionheaders | Applications | |
dc.identifier.doi | 10.2312/eurova.20221075 | |
dc.identifier.pages | 25-29 | |
dc.identifier.pages | 5 pages | |