Abstract
The application of smart programs and services in the medical field is nowadays a reality that guarantees patients a quality healthcare. In the present article, it is proposed the development of a Clinical Decision Support System (CDSS) in order to diagnose a set of “fuzzy diseases”.This concept refers to the diseases that are not diagnosable through a concrete clinical test or symptom. Then, the diagnosis of a “fuzzy diseases” set is based in the exclusion of symptoms and tests results, due to the similarity between them. For dealing with these inconveniences, in this paper it has been designed a reasoning method which uses mainly a theory about the conceptual categorization from the Psychology field [1, 2] and the prototypes concept of Zadeh [3]. Through the use of this model, it was obtained a satisfactory result in the evaluation of patients.
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Romero-Córdoba, R., Olivas, J.Á., Romero, F.P., Alonso-Gómez, F. (2015). Clinical Decision Support System for the Diagnosis and Treatment of Fuzzy Diseases. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_12
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