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Sobre

Sobre

Nascida nos anos 90, completei o meu percurso académico no Instituto de Engenharia da Universidade do Porto(ISEP) com o grau de Mestre no curso de Sistemas Computacionais.

Atualmente, encontro-me a trabalhar numa bolsa de investigação no LIAAD, no projeto NanoSTIMA, onde estou a desenvolver métodos para prever com exatidão mudanças no estado de um paciente com insuficiência renal. 

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Ana Rita Nogueira
  • Cluster

    Informática
  • Cargo

    Assistente de Investigação
  • Desde

    01 outubro 2016
001
Publicações

2021

Causal discovery in machine learning: Theories and applications

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

Publicação
Journal of Dynamics & Games

Abstract

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

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

Publicação
BigMine@KDD

Abstract

2020

Improving Prediction with Causal Probabilistic Variables

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

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

Abstract

2018

Improving acute kidney injury detection with conditional probabilities

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

Publicação
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

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

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

Abstract