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Title: Antiphospholipid Syndrome Risk Evaluation
Authors: Vilhena, João
Vicente, Henrique
Martins, M. Rosário
Grañeda, José M.
Caldeira, Filomena
Gusmão, Rodrigo
Neves, João
Neves, José
Editors: Rocha, Álvaro
Correia, Ana Maria
Adeli, Hojjat
Reis, Luís
Teixeira, Marcelo
Keywords: Antiphospholipid Syndrome
Logic Programming
Artificial Neural Networks
Knowledge Representation and Reasoning
Issue Date: 2016
Publisher: Springer International Publishing
Citation: Vilhena, J., Vicente, H., Martins, M. R., Grañeda, J., Caldeira, F., Gusmão, R., Neves, J. & Neves, J. Antiphospholipid Syndrome Risk Evaluation. In Á. Rocha, A.M. Correia, H. Adeli, L.P. Reis & M.M. Teixeira, Eds., New Advances in Information Systems and Technologies – Vol. 1, Advances in Intelligent Systems and Computing, Vol. 444, pp. 157–167, Springer International Publishing, Cham, Switzerland, 2016.
Abstract: The antiphospholipid syndrome is an acquired autoimmune disorder produced by high titers of antiphospholipid antibodies that cause both arterial and veins thrombosis as well as pregnancy-related complications and morbidity, as clinical manifestations. This autoimmune hypercoagulable state, often associated with coronary artery disease and recurrent Acute Myocardium Infraction, has severe consequences for the patients, being one of the main causes of thrombotic disorders and death. Therefore, it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Despite the updated of the APS classification published as Sydney criteria, diagnosis of this syndrome remains challenging. Further research on clinically relevant antibodies and standardization of their quantification are required to improve clinical risk assessment in APS. This work will focus on the development of a diagnosis support system to antiphospholipid syndrome, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks. The proposed model allowed to improve the diagnosis, classifying properly the patients that really presented this pathology (sensitivity about 92%) as well as classifying the absence of APS (specificity ranging from 89% to 94%).
ISBN: 978-3-319-31231-6
ISSN: 2194-5357
Type: bookPart
Appears in Collections:MED - Publicações - Capítulos de Livros
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