Challenges and Opportunities of Machine Learning for Clinical and Omics Data

Pestarino, Luca (2022) Challenges and Opportunities of Machine Learning for Clinical and Omics Data, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Data science and computation, 33 Ciclo.
Documenti full-text disponibili:
[img] Documento PDF (English) - Accesso riservato fino a 21 Marzo 2024 - Richiede un lettore di PDF come Xpdf o Adobe Acrobat Reader
Disponibile con Licenza: Creative Commons Attribution Non-commercial No Derivatives 4.0 (CC BY-NC-ND 4.0) .
Download (3MB) | Contatta l'autore


Clinical and omics data are a promising field of application for machine learning techniques even though these methods are not yet systematically adopted in healthcare institutions. Despite artificial intelligence has proved successful in terms of prediction of pathologies or identification of their causes, the systematic adoption of these techniques still presents challenging issues due to the peculiarities of the analysed data. The aim of this thesis is to apply machine learning algorithms to both clinical and omics data sets in order to predict a patient's state of health and get better insights on the possible causes of the analysed diseases. In doing so, many of the arising issues when working with medical data will be discussed while possible solutions will be proposed to make machine learning provide feasible results and possibly become an effective and reliable support tool for healthcare systems.

Tipologia del documento
Tesi di dottorato
Pestarino, Luca
Dottorato di ricerca
Settore disciplinare
Settore concorsuale
Parole chiave
Machine learning, Supervised learning, Unsupervised learning, Feature selection, Clinical data, Multiomics
Data di discussione
21 Marzo 2022

Altri metadati

Gestione del documento: Visualizza la tesi