Towards collaborative computational models for predicting and understanding complex degenerative disease trajectories

This field, which took the name Artificial Intelligence (Al), comprises several strategies to enable machines to perform different tasks. Research in this field experienced a rush for performance, striving to obtain the best-performing model possible, which led to Deep Learning (DL).Unfortunately, focusing only on performance compromises other aspects, such as the transparency of the model.

Most ML and DL models also struggle with small datasets, missing information, and the presence of noise in the data. These limitations can impair their performance despite their strong theoretical foundations.<br><br>The contributions presented in this thesis focus on these constraints, providing a framework that can perform under such conditions while being inherently explainable.The main application of this research is healthcare, due to the typical limitations in the quality and quantity of data inherent to this field, especially for rare conditions.This thesis explored a novel approach to classification named CACTUS (the Comprehensive Abstraction and Classification Tool for Uncovering Structures), which shows promising results in overcoming some limitations in ML, identifying and addressing open<br>challenges in classification, with an emphasis on AMO diagnosis.<br><br><i>Thesis is embargoed until 31 July 2028.</i>