Abstract
Time dependence in medical diagnosis is important since, frequently, symptoms evolve over time, thus, changing with the progression of an illness. Taking into consideration that medical information may be vague, missing and/or conflicting during the diagnostic procedure, a new type of Fuzzy Cognitive Maps (FCMs), the soft computing technique that can handle uncertainty to infer a result, have been developed for Medical Diagnosis. Here, a method to enhance the FCM behaviour is proposed introducing time units that can follow disease progression. An example from the pulmonary field is described.
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Bourgani, E., Stylios, C.D., Manis, G., Georgopoulos, V.C. (2014). Time Dependent Fuzzy Cognitive Maps for Medical Diagnosis. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_47
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DOI: https://doi.org/10.1007/978-3-319-07064-3_47
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07063-6
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