<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-14T04:12:54Z</responseDate><request verb="GetRecord" identifier="oai:www.recercat.cat:10230/42547" metadataPrefix="qdc">https://recercat.cat/oai/request</request><GetRecord><record><header><identifier>oai:recercat.cat:10230/42547</identifier><datestamp>2025-12-19T20:30:07Z</datestamp><setSpec>com_2072_6</setSpec><setSpec>col_2072_452954</setSpec></header><metadata><qdc:qualifieddc xmlns:qdc="http://dspace.org/qualifieddc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://purl.org/dc/elements/1.1/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dc.xsd http://purl.org/dc/terms/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dcterms.xsd http://dspace.org/qualifieddc/ http://www.ukoln.ac.uk/metadata/dcmi/xmlschema/qualifieddc.xsd">
   <dc:title>A computational and visualisation tool for investigating associations between cardiac&#xd;
radiomics, risk factors and clinical data</dc:title>
   <dc:creator>Phloyngam, Naphatthara</dc:creator>
   <dc:subject>Imatges tridimensionals en biologia</dc:subject>
   <dc:subject>Sistema cardiovascular -- Malalties</dc:subject>
   <dc:subject>Aprenentatge automàtic</dc:subject>
   <dc:subject>Cardiovascular disease</dc:subject>
   <dc:subject>Radiomics</dc:subject>
   <dc:subject>Machine learning</dc:subject>
   <dc:subject>Visualization</dc:subject>
   <dcterms:abstract>Treball fi de màster de: Master in Intelligent Interactive Systems</dcterms:abstract>
   <dcterms:abstract>Tutors: Karim Lekadir, Carlos Martín Isla</dcterms:abstract>
   <dcterms:abstract>Identifying the correlations between radiomics and additional medical, health and&#xd;
lifestyle factors such as sex, age, BMI, etc. may help in discovering the significant&#xd;
hidden patterns of data and realizing the causes of the diseases. Also, knowing the&#xd;
risks in advance is a useful piece of supplementary information which may be used&#xd;
in addition to medical intervention resulting in preventative measures to reduce the&#xd;
level of risk or to control prescribed treatments.&#xd;
In the radiomics and the clinical outcomes datasets, it is hard to identify their correlations&#xd;
due to the complexity of data, computationally expensive and high number&#xd;
of possible combination among the choices. Therefore, data pre-processing to keep&#xd;
only the potential features and data cleaning to deal with missing or non-informative&#xd;
values are mandatory steps to perform. In addition, applying the powerful Machine&#xd;
Learning algorithms help to bring the results that even non-specialists in the field&#xd;
could discover and understand.&#xd;
This thesis facilitates the discovery of these correlations through the design and&#xd;
development of an intuitive and interactive web-based tool which dynamically displays&#xd;
the radiomic feature set alongside the additional medical, health and lifestyle&#xd;
factors feature set based on the contents of radiomics and clinical data files. The&#xd;
tool also provides a visualization of the correlation results in an easy to interpret&#xd;
and meaningful way allowing for effective exploration of any correlations in addition&#xd;
to cardiovascular risk score calculation.</dcterms:abstract>
   <dcterms:issued>2019-10-29T11:01:42Z</dcterms:issued>
   <dcterms:issued>2019-10-29T11:01:42Z</dcterms:issued>
   <dcterms:issued>2019</dcterms:issued>
   <dc:type>info:eu-repo/semantics/masterThesis</dc:type>
   <dc:rights>Atribución-NoComercial-SinDerivadas 3.0 España</dc:rights>
   <dc:rights>http://creativecommons.org/licenses/by-nc-nd/3.0/es/</dc:rights>
   <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
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