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Difference between revisions of "Fuzzy language logic"
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| autore3 = Flavio Frisardi | | autore3 = Flavio Frisardi | ||
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In this chapter, we delve into the cognitive limits of diagnostic reasoning, focusing on how knowledge in medical science is bound by time and context, represented by two parameters: <math>KB_t</math> (time-dependent knowledge base) and <math>KB_c</math> (context-dependent knowledge base). These parameters emphasize how scientific knowledge evolves and is shaped by the specific context in which it operates. Through practical examples, such as research on Temporomandibular Disorders (TMD) and Orofacial Pain (OP), we illustrate the interconnectedness of scientific fields and the significant drop in knowledge integration when topics are combined. | [[File:Fuzzy1.jpg|right|200x200px]] | ||
'''Abstract:''' In this chapter, we delve into the cognitive limits of diagnostic reasoning, focusing on how knowledge in medical science is bound by time and context, represented by two parameters: <math>KB_t</math> (time-dependent knowledge base) and <math>KB_c</math> (context-dependent knowledge base). These parameters emphasize how scientific knowledge evolves and is shaped by the specific context in which it operates. Through practical examples, such as research on Temporomandibular Disorders (TMD) and Orofacial Pain (OP), we illustrate the interconnectedness of scientific fields and the significant drop in knowledge integration when topics are combined. | |||
The discussion moves from the limitations of classical logic to the broader framework of probabilistic reasoning, and eventually to fuzzy logic. We examine how classical logic, with its binary nature, is inadequate for medical diagnosis, where uncertainty is inherent. By introducing fuzzy logic, a multivalent approach, we show how it is better suited to handling the complexities and uncertainties of clinical cases, such as that of Mary Poppins. | The discussion moves from the limitations of classical logic to the broader framework of probabilistic reasoning, and eventually to fuzzy logic. We examine how classical logic, with its binary nature, is inadequate for medical diagnosis, where uncertainty is inherent. By introducing fuzzy logic, a multivalent approach, we show how it is better suited to handling the complexities and uncertainties of clinical cases, such as that of Mary Poppins. | ||