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Title: An Artificial Intelligence Approach to Thrombophilia Risk
Authors: Vilhena, João
Vicente, Henrique
Martins, M. Rosário
Grañeda, José M.
Caldeira, Filomena
Gusmão, Rodrigo
Neves, João
Neves, José
Keywords: Artificial Neuronal Networks
Decision Support System
Degree of Confidence
Knowledge Representation and Reasoning
Logic Programming
Quality of Information
Venous Thromboembolism
Issue Date: 2017
Publisher: IGI Global
Citation: Vilhena, J., Vicente, H., Martins, M.R., Grañeda, J., Caldeira, F., Gusmão, R., Neves, J. & Neves, J., An Artificial Intelligence Approach to Thrombophilia Risk. International Journal of Reliable and Quality E-Healthcare, 6 (2): 49–69, 2017.
Abstract: Thrombophilia stands for a genetic or an acquired tendency to hypercoagulable states, frequently as venous thrombosis. Venous thromboembolism, represented mainly by deep venous thrombosis and pulmonary embolism, is often a chronic illness, associated with high morbidity and mortality. Therefore, it is crucial to identify the cause of the disease, the most appropriate treatment, the length of treatment or prevent a thrombotic recurrence. This work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a computational framework based on Artificial Neural Networks. The proposed model has been quite accurate in the assessment of thrombophilia predisposition (accuracy close to 95%). Furthermore, the model classified properly the patients that really presented the pathology, as well as classifying the disease absence (sensitivity and specificity higher than 95%).
2160-956X (Online)
ISSN: 2160-9551 (Print)
Type: article
Appears in Collections:HERCULES - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
QUI - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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