1 code implementation • 8 Aug 2022 • Sérgio Machado, Anirudh Sridhar, Paulo Gil, Jorge Henriques, José M. F. Moura, Augusto Santos
This renders the features amenable to train a variety of classifiers to perform causal inference.
no code implementations • 15 Oct 2021 • Francisco Valente, Jorge Henriques, Simão Paredes, Teresa Rocha, Paulo de Carvalho, João Morais
In order to achieve the mentioned goals, a three-step methodology was developed: several rules were created by dichotomizing risk factors; such rules were trained with a machine learning classifier to predict the acceptance degree of each rule (the probability that the rule is correct) for each patient; that information was combined and used to compute the risk of mortality and the reliability of such prediction.
no code implementations • 15 Jul 2021 • Francisco Valente, Simão Paredes, Jorge Henriques
In this study, we present a novel clinical decision support system and discuss its interpretability-related properties.
no code implementations • 15 Jun 2021 • Francisco Valente, Jorge Henriques, Simão Paredes, Teresa Rocha, Paulo de Carvalho, João Morais
Some procedures consider a simplification of ETs, using heuristic approaches to select an optimal reduced set of decision rules.