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               <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>
               <dc:description>Treball fi de màster de: Master in Intelligent Interactive Systems</dc:description>
               <dc:description>Tutors: Karim Lekadir, Carlos Martín Isla</dc:description>
               <dc:description>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.</dc:description>
               <dc:date>2019-10-29T11:01:42Z</dc:date>
               <dc:date>2019-10-29T11:01:42Z</dc:date>
               <dc:date>2019</dc:date>
               <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>
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