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Bachelor thesisPublication date
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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/212512
Application of multiblock techniques in metabolomic and clinical data for the prediction of ventilatory therapies in Covid-19 patients
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Abstract
Multiblock techniques, as the name suggests, are analytical methods that handle datasets
organized into multiple blocks, each of them representing a set of variables with similar nature.
These methods aim to model relationships across these blocks, delving into the internal structure
of the data to uncover patterns that might not be apparent when analysing them individually. In this
thesis, regularised CCA, PLS and multiblock-PLS-DA (DIABLO) have been applied to Covid-19
patients’ data to study the behaviour and relationships between two sets of variables, one
containing metabolomic measures and the other evaluating different clinical features. Additionally,
two different approaches have been proposed and discussed to incorporate multiblock analysis
into the prediction of the failure of non-invasive respiratory support in Covid-19 patients. This has
allowed not only the exploration of the relationship net between blocks but also the identification of
similarities and correlations among the relevant variables in a predictive context. At the end,
multiblock techniques have shown their potential to extract valuable information from the dataset
and to optimize and maximize the classification procedure, leading to the identification of three
variables (anthranilic acid, octanoic acid and malate/succinate ratio) that allow to successfully
predict the failure of non-invasive respiratory support in Covid-19 patients.
Description
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2023-2024. Tutor/Director: Luis Fernández Romero ; Luis Fernández Romero,
Santiago Marco Colás
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PORTA VALERO, Miquel. Application of multiblock techniques in metabolomic and clinical data for the prediction of ventilatory therapies in Covid-19 patients. [consulted: 12 of August of 2026]. Available at: https://hdl.handle.net/2445/212512