2016
Neves, J.; Gonçalves, N.; Oliveira, R.; Gomes, S.; Neves, J.; Macedo, J.; Abelha, A.; Analide, C.; Machado, J.; Santos, M. F.; Vicente, H.
Screening a case base for stroke disease detection Proceedings Article
Em: F., Quintian H. Troncoso A. Martinez-Alvarez (Ed.): pp. 3-13, Springer Verlag, 2016, ISSN: 03029743, (cited By 2; Conference of 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016 ; Conference Date: 8 April 2016 Through 20 April 2016; Conference Code:173819).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Computation theory; Computer circuits; Decision support systems; Diagnosis; Intelligent systems; Knowledge representation; Logic programming; Quality control; Reconfigurable hardware; Risk assessment, Case based reasoning, Case-based reasoning approaches; Clustering methods; Degree of confidence; Disease detection; Knowledge representation and reasoning; Quality of informations (QoI); Similarity analysis; Universe of discourse
@inproceedings{Neves20163,
title = {Screening a case base for stroke disease detection},
author = {J. Neves and N. Gonçalves and R. Oliveira and S. Gomes and J. Neves and J. Macedo and A. Abelha and C. Analide and J. Machado and M. F. Santos and H. Vicente},
editor = {Quintian H. Troncoso A. Martinez-Alvarez F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964056771&doi=10.1007%2f978-3-319-32034-2_1&partnerID=40&md5=8b11d528fb7c9db682be64a47fb6730e},
doi = {10.1007/978-3-319-32034-2_1},
issn = {03029743},
year = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9648},
pages = {3-13},
publisher = {Springer Verlag},
abstract = {Stroke stands for one of the most frequent causes of death, without distinguishing age or genders. Despite representing an expressive mortality figure, the disease also causes long-term disabilities with a huge recovery time, which goes in parallel with costs. However, stroke and health diseases may also be prevented considering illness evidence. Therefore, the present work will start with the development of a decision support system to assess stroke risk, centered on a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with a Case Based Reasoning (CBR) approach to computing. Indeed, and in order to target practically the CBR cycle, a normalization and an optimization phases were introduced, and clustering methods were used, then reducing the search space and enhancing the cases retrieval one. On the other hand, and aiming at an improvement of the CBR theoretical basis, the predicates` attributes were normalized to the interval 0…1, and the extensions of the predicates that match the universe of discourse were rewritten, and set not only in terms of an evaluation of its Quality-of-Information (QoI), but also in terms of an assessment of a Degree-of-Confidence (DoC), a measure of oneʼs confidence that they fit into a given interval, taking into account their domains, i.e., each predicate attribute will be given in terms of a pair (QoI, DoC), a simple and elegant way to represent data or knowledge of the type incomplete, self-contradictory, or even unknown. © Springer International Publishing Switzerland 2016.},
note = {cited By 2; Conference of 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016 ; Conference Date: 8 April 2016 Through 20 April 2016; Conference Code:173819},
keywords = {Artificial intelligence; Computation theory; Computer circuits; Decision support systems; Diagnosis; Intelligent systems; Knowledge representation; Logic programming; Quality control; Reconfigurable hardware; Risk assessment, Case based reasoning, Case-based reasoning approaches; Clustering methods; Degree of confidence; Disease detection; Knowledge representation and reasoning; Quality of informations (QoI); Similarity analysis; Universe of discourse},
pubstate = {published},
tppubtype = {inproceedings}
}
Stroke stands for one of the most frequent causes of death, without distinguishing age or genders. Despite representing an expressive mortality figure, the disease also causes long-term disabilities with a huge recovery time, which goes in parallel with costs. However, stroke and health diseases may also be prevented considering illness evidence. Therefore, the present work will start with the development of a decision support system to assess stroke risk, centered on a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with a Case Based Reasoning (CBR) approach to computing. Indeed, and in order to target practically the CBR cycle, a normalization and an optimization phases were introduced, and clustering methods were used, then reducing the search space and enhancing the cases retrieval one. On the other hand, and aiming at an improvement of the CBR theoretical basis, the predicates` attributes were normalized to the interval 0…1, and the extensions of the predicates that match the universe of discourse were rewritten, and set not only in terms of an evaluation of its Quality-of-Information (QoI), but also in terms of an assessment of a Degree-of-Confidence (DoC), a measure of oneʼs confidence that they fit into a given interval, taking into account their domains, i.e., each predicate attribute will be given in terms of a pair (QoI, DoC), a simple and elegant way to represent data or knowledge of the type incomplete, self-contradictory, or even unknown. © Springer International Publishing Switzerland 2016.