Tricobezoar de estómago / Gastric Trichobezoar Rapunzel Syndrome

Trichobezoars are an unusual pathology that appears generally in young adolescents associated with psychiatric disorders. The clinical presentation is very varied. The diagnosis is suspected by the clinical records of teen agers with trichophagia and trichotillomania and the digestive disorders are confirmed by the results of the endoscopy, the same as with images toward the therapeutic management. Undowbtly the treatment is surgical, and continuation with the psychiatric treatment is essential to avoid a recidival of the disease. Bezoar is a concretion formed in the alimentary tract, and according to the substances forming the ball, we find trichobezoar (foodball). The Rapunzel syndrome is an unusual complication of individual bezoar. When the trichobezoar located in the stomach extends through the pylorus into the small intestine and the right colon, is known as Rapunzel syndrome, that is an extremely rare gastric condition in humans. It is a rare form of trichobezoar, occurring in psychiatric patients with the trichobezoar (hairball) located in the stomach. The syndrome is named after the long haired girl Rapunzel in the fairy tale of the brothers Grimm. Most bezoars in teen agers are trichobezoars from swallowed hair. A 28-year-old patient is presented, with abdominal pain and vomiting, on the general physical examination the patient revealed a severe weight loss. Later on, through a gastrostomy, appeared the trichobezoar, being removed with good postsurgical resullts

Fuzzy decision trees: issues and methods.

Decision trees are one of the most popular choices for learning and reasoning from feature-based examples. They have undergone a number of alterations to deal with language and measurement uncertainties. We present another modification, aimed at combining symbolic decision trees with approximate reasoning offered by fuzzy representation. The intent is to exploit complementary advantages of both: popularity in applications to learning from examples, high knowledge comprehensibility of decision trees, and the ability to deal with inexact and uncertain information of fuzzy representation. The merger utilizes existing methodologies in both areas to full advantage, but is by no means trivial. In particular, knowledge inferences must be newly defined for the fuzzy tree. We propose a number of alternatives, based on rule-based systems and fuzzy control. We also explore capabilities that the new framework provides. The resulting learning method is most suitable for stationary problems, with both numerical and symbolic features, when the goal is both high knowledge comprehensibility and gradually changing output. We describe the methodology and provide simple illustrations.