An Artificial Intelligence Approach to Thrombophilia Risk

João Vilhena, Henrique Vicente, M. Rosário Martins, José Grañeda, Filomena Caldeira, Rodrigo Gusmão, João Neves and José Neves
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João Vilhena: Universidade de Évora, Évora, Portugal
Henrique Vicente: Universidade de Évora, Évora, Portugal
M. Rosário Martins: Universidade de Évora, Évora, Portugal
José Grañeda: Hospital do Espírito Santo de Évora, Évora, Portugal
Filomena Caldeira: Hospital do Espírito Santo de Évora, Évora, Portugal
Rodrigo Gusmão: Hospital do Espírito Santo de Évora, Évora, Portugal
João Neves: Drs. Nicolas & Asp, Dubai, UAE
José Neves: Universidade do Minho, Braga, Portugal

International Journal of Reliable and Quality E-Healthcare (IJRQEH), 2017, vol. 6, issue 2, 49-69

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%).

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jrqeh0:v:6:y:2017:i:2:p:49-69

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