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About

I am a lecturer in the Department of Computer Science, School of Sciences of University of Porto, Portugal. I obtained a B.Sc. degree in Computer Science from State University of Rio de Janeiro, Brazil, in 1985, and an M.Sc. degree in the Systems Engineering and Computer Science department of Federal University of Rio de Janeiro, Brazil, in 1988. My Ph.D. degree was obtained from Bristol University, UK, in 1995. In 1998, I started as a lecturer in the Department of Systems Engineering and Computer Science of COPPE, an institution for postgraduate studies in Engineering, at Federal University of Rio de Janeiro, where I taught courses on Operating Systems, Concurrent Programming and Topics on High Performance Computing, at M.Sc. and Ph.D. levels, and Artificial Intelligence and Logic Programming, at undergraduate level. In Februrary 2007 I moved to Portugal where I am now located. During the periods between October 2001 and December 2002, April 2004 and March 2005, Aug 2010 and Feb 2011, and Oct 2014 and Mar 2015, I worked as a visiting researcher at University of Wisconsin-Madison, USA, in the department of Biostatistics and Medical Informatics, and at the Radiology Department of the School of Sciences and Public Health. During these periods, I worked for machine learning projects funded by NSF, DARPA and American Air Force (projects COLLEAGUE, EELD and EAGLE), and NLM (Project ABLe) and started to work with applications that demanded a huge amount of resources. At this time, I had the opportunity to work with the Condor team, and to largely use the Condor resource manager to run experiments. My main research areas are Logic programming, Inductive Logic Programming, and Parallel Logic Programming systems. I served as Program Comittee member of several workshops and conferences in these areas. I supervised several M.Sc. and Ph.D. students in these areas. I have more than 80 publications in conferences and journals. I also participated or was the principal investigator of several projects funded by CNPq (Brazil), FCT (Portugal) and the EU. I am a member of the EELA (E-science grid facility for Europe and Latin America) initiative, whose main objective is to promote and maintain the infrastructure of hardware and software between Europe and Latin America. Currently, I have been working on machine learning techniques based on Inductive Logic programming, but still using parallelzation and grid environments to be able to perform machine learning experiments.

Interest
Topics
Details

Details

  • Name

    Inês Dutra
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    01st January 2009
005
Publications

2022

Design and Development of an Intelligent Clinical Decision Support System Applied to the Evaluation of Breast Cancer Risk

Authors
Casal-Guisande, M; Comesaña-Campos, A; Dutra, I; Cerqueiro-Pequeño, J; Bouza-Rodríguez, J;

Publication
Journal of Personalized Medicine

Abstract
Breast cancer is currently one of the main causes of death and tumoral diseases in women. Even if early diagnosis processes have evolved in the last years thanks to the popularization of mammogram tests, nowadays, it is still a challenge to have available reliable diagnosis systems that are exempt of variability in their interpretation. To this end, in this work, the design and development of an intelligent clinical decision support system to be used in the preventive diagnosis of breast cancer is presented, aiming both to improve the accuracy in the evaluation and to reduce its uncertainty. Through the integration of expert systems (based on Mamdani-type fuzzy-logic inference engines) deployed in cascade, exploratory factorial analysis, data augmentation approaches, and classification algorithms such as k-neighbors and bagged trees, the system is able to learn and to interpret the patient’s medical-healthcare data, generating an alert level associated to the danger she has of suffering from cancer. For the system’s initial performance tests, a software implementation of it has been built that was used in the diagnosis of a series of patients contained into a 130-cases database provided by the School of Medicine and Public Health of the University of Wisconsin-Madison, which has been also used to create the knowledge base. The obtained results, characterized as areas under the ROC curves of 0.95–0.97 and high success rates, highlight the huge diagnosis and preventive potential of the developed system, and they allow forecasting, even when a detailed and contrasted validation is still pending, its relevance and applicability within the clinical field.

2022

Design and Development of an Intelligent Clinical Decision Support System Applied to the Evaluation of Breast Cancer Risk

Authors
Casal-Guisande M.; Comesaña-Campos A.; Dutra I.; Cerqueiro-Pequeño J.; Bouza-Rodríguez J.B.;

Publication
Journal of Personalized Medicine

Abstract
Breast cancer is currently one of the main causes of death and tumoral diseases in women. Even if early diagnosis processes have evolved in the last years thanks to the popularization of mammogram tests, nowadays, it is still a challenge to have available reliable diagnosis systems that are exempt of variability in their interpretation. To this end, in this work, the design and development of an intelligent clinical decision support system to be used in the preventive diagnosis of breast cancer is presented, aiming both to improve the accuracy in the evaluation and to reduce its uncertainty. Through the integration of expert systems (based on Mamdani-type fuzzy-logic inference engines) deployed in cascade, exploratory factorial analysis, data augmentation approaches, and classification algorithms such as k-neighbors and bagged trees, the system is able to learn and to interpret the patient’s medical-healthcare data, generating an alert level associated to the danger she has of suffering from cancer. For the system’s initial performance tests, a software implementation of it has been built that was used in the diagnosis of a series of patients contained into a 130-cases database provided by the School of Medicine and Public Health of the University of Wisconsin-Madison, which has been also used to create the knowledge base. The obtained results, characterized as areas under the ROC curves of 0.95–0.97 and high success rates, highlight the huge diagnosis and preventive potential of the developed system, and they allow forecasting, even when a detailed and contrasted validation is still pending, its relevance and applicability within the clinical field.

2021

Simple Matrix Factorization Collaborative Filtering for Drug Repositioning on Cell Lines

Authors
Carrera, I; Tejera, E; Dutra, I;

Publication
Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021, Volume 5: HEALTHINF, Online Streaming, February 11-13, 2021.

Abstract

2021

Predictive Maintenance for Sensor Enhancement in Industry 4.0

Authors
Silva, C; da Silva, MF; Rodrigues, A; Silva, J; Costa, VS; Jorge, A; Dutra, I;

Publication
Recent Challenges in Intelligent Information and Database Systems - Communications in Computer and Information Science

Abstract

2021

Quantum Binary Classification (Student Abstract)

Authors
Silva, C; Aguiar, A; Dutra, I;

Publication
Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021

Abstract

Supervised
thesis

2019

Towards Improving the Search for Multi-Relational Concepts in ILP

Author
Alberto José Rajão Barbosa

Institution
UP-FCUP

2019

Exascale computing with custom Linear Mixed Model kernels and GPU accelerators for Genomic Wide Association Studies and personalized medicine

Author
Christopher David Harrison

Institution
UP-FCUP

2017

Weighted Multiple Kernel Learning for Breast Cancer Diagnosis applied to Mammograms

Author
Tiago André Guedes Santos

Institution
UP-FCUP

2017

Execução e Gestão de Aplicações Conteinerizadas

Author
Diogo Cristiano dos Santos Reis

Institution
UP-FCUP

2017

Improving the search for multi-relational concepts in ILP

Author
Alberto José Rajão Barbosa

Institution
UP-FCUP