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An Artificial Intelligence Approach to Thrombophilia Risk

2017; IGI Global; Volume: 6; Issue: 2 Linguagem: Inglês

10.4018/ijrqeh.2017040105

2160-956X

João Vilhena, Henrique Vicente, M. Rosário Martins, José M. Grañeda, Filomena Caldeira, Rodrigo Gusmão, João Neves, José Neves,

Topic Modeling

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%).