Details
Name
Carlos FerreiraCluster
Computer ScienceRole
Senior ResearcherSince
01st January 2010
Nationality
PortugalCentre
Artificial Intelligence and Decision SupportContacts
+351220402963
carlos.ferreira@inesctec.pt
2022
Authors
Oliveira, J; Renna, F; Costa, PD; Nogueira, M; Oliveira, C; Ferreira, C; Jorge, A; Mattos, S; Hatem, T; Tavares, T; Elola, A; Rad, AB; Sameni, R; Clifford, GD; Coimbra, MT;
Publication
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Abstract
2022
Authors
Oliveira, J; Nogueira, DM; Ferreira, CA; Jorge, AM; Coimbra, MT;
Publication
44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022
Abstract
2022
Authors
Nogueira, AR; Ferreira, CA; Gama, J;
Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022
Abstract
In hospital and after ICU discharge deaths are usual, given the severity of the condition under which many of them are admitted to these wings. Because of this, there is an urge to identify and follow these cases closely. Furthermore, as ICU data is usually composed of variables measured in varying time intervals, there is a need for a method that can capture causal relationships in this type of data. To solve this problem, we propose ItsPC, a causal Bayesian network that can model irregular multivariate time-series data. The preliminary results show that ItsPC creates smaller and more concise networks while maintaining the temporal properties. Moreover, its irregular approach to time-series can capture more relationships with the target than the Dynamic Bayesian Networks.
2022
Authors
Nogueira, AR; Ferreira, CA; Gama, J;
Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE
Abstract
Typically, classification algorithms use correlation analysis to make decisions. However, these decisions and the models they learn are not easily understandable for the typical user. Causal discovery is the field that studies the means to find causal relationships in observational data. Although highly interpretable, causal discovery algorithms tend to not perform so well in classification problems. This paper aims to propose a hybrid decision tree approach (SC tree) that mixes causal discovery with correlation analysis through the implementation of a custom metric to split the data in the tree's construction (Semi-causal gain ratio). In the results, the proposed methodology obtained a significant performance improvement (11.26% mean error rate) when compared to several causal baselines CDT-PS (23.67% ) and CDT-SPS (25.14%), matching closely the performance of J48 (10.20%), used as a correlation baseline, in ten binary data sets. Besides, when compared with PC in discrete data sets, the proposed approach obtained substantial improvement (16.17% against 28.07% in terms of mean error rate).
2021
Authors
Oliveira, M; Oliveira, J; Camacho, R; Ferreira, C;
Publication
BIOSIGNALS: PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES - VOL 4: BIOSIGNALS
Abstract
Supervised Thesis
2021
Author
MARCELO ZEFERINO VIEIRA FERREIRA
Institution
IPP-ISEP
2021
Author
DOMINGOS BERNARDINO PEREIRA DA COSTA
Institution
IPP-ISEP
2020
Author
RICARDO JORGE COELHO LIMA
Institution
IPP-ISEP
2020
Author
XAVIER DOS SANTOS SILVA
Institution
IPP-ISEP
2020
Author
ANDRÉ MANUEL GONÇALVES PORTELA FALHAS DA COSTA
Institution
IPP-ISEP
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.