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An Artificial Intelligence Approach to Thrombophilia Risk

An Artificial Intelligence Approach to Thrombophilia Risk

João Vilhena, Henrique Vicente, M. Rosário Martins, José Grañeda, Filomena Caldeira, Rodrigo Gusmão, João Neves, José Neves
Copyright: © 2017 |Volume: 6 |Issue: 2 |Pages: 21
ISSN: 2160-9551|EISSN: 2160-956X|EISBN13: 9781522515128|DOI: 10.4018/IJRQEH.2017040105
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MLA

Vilhena, João, et al. "An Artificial Intelligence Approach to Thrombophilia Risk." IJRQEH vol.6, no.2 2017: pp.49-69. http://doi.org/10.4018/IJRQEH.2017040105

APA

Vilhena, J., Vicente, H., Martins, M. R., Grañeda, J., Caldeira, F., Gusmão, R., Neves, J., & Neves, J. (2017). An Artificial Intelligence Approach to Thrombophilia Risk. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 6(2), 49-69. http://doi.org/10.4018/IJRQEH.2017040105

Chicago

Vilhena, João, et al. "An Artificial Intelligence Approach to Thrombophilia Risk," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 6, no.2: 49-69. http://doi.org/10.4018/IJRQEH.2017040105

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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%).

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