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About

About

Born in the 90s, I completed my academic course at the Instituto de Engenharia da Universidade do Porto (ISEP) with a Master's degree in Computer Systems.

I am currently working on a research grant at LIAAD in the NanoSTIMA project where I am developing methods to accurately predict changes in the state of a patient with kidney disease.

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Details

001
Publications

2020

Proceedings of the 8th International Workshop on Big Data, IoT Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications co-located with 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Alaska, August 4-8, 2019

Authors
Bifet, A; Berlingerio, M; Gama, J; Read, J; Nogueira, AR;

Publication
BigMine@KDD

Abstract

2020

Improving Prediction with Causal Probabilistic Variables

Authors
Nogueira, AR; Gama, J; Ferreira, CA;

Publication
Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27-29, 2020, Proceedings

Abstract

2020

Improving Prediction with Causal Probabilistic Variables

Authors
Nogueira, AR; Gama, J; Ferreira, CA;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
The application of feature engineering in classification problems has been commonly used as a means to increase the classification algorithms performance. There are already many methods for constructing features, based on the combination of attributes but, to the best of our knowledge, none of these methods takes into account a particular characteristic found in many problems: causality. In many observational data sets, causal relationships can be found between the variables, meaning that it is possible to extract those relations from the data and use them to create new features. The main goal of this paper is to propose a framework for the creation of new supposed causal probabilistic features, that encode the inferred causal relationships between the target and the other variables. In this case, an improvement in the performance was achieved when applied to the Random Forest algorithm. © 2020, The Author(s).

2018

Improving acute kidney injury detection with conditional probabilities

Authors
Nogueira, AR; Ferreira, CA; Gama, J;

Publication
Intell. Data Anal.

Abstract
The Acute Kidney Injury (AKI), is a disease that affects the kidneys and is characterized by the rapid deterioration of these organs, usually associated with a pre-existing critical illness. Being an acute disease, time is a key element in the prevention. By anticipating a patient's state transition, we are preventing future complications in his health, such as the development of a chronic disease or loss of an organ, in addition to decreasing the amount of money spent on the patient's care. The main goal of this paper is to address the problem of correctly predicting the illness path in various patients by studying different methodologies to predict this disease and propose new distinct approaches based on this idea of improving the performance of the classification. Through the comparison of five different approaches (Markov Chain Model ICU Specialists, Markov Chain Model Features, Markov Chain Model Conditional Features, Markov Chain Model and Random Forest), we came to the conclusion that the application of conditional probabilities to this problem produces a more accurate prediction, based on common inputs.

2017

Acute Kidney Injury Detection: An Alarm System to Improve Early Treatment

Authors
Nogueira, AR; Ferreira, CA; Gama, J;

Publication
Foundations of Intelligent Systems - 23rd International Symposium, ISMIS 2017, Warsaw, Poland, June 26-29, 2017, Proceedings

Abstract