Pet) Herbal Medicine

In this paper, we present a soft computing modelof decision support systems for diagnosing diseases andprescribing herbal prescriptions by oriental medicine.Inputs to the model are severities of observed symptoms onpatients and outputs from the model are a diagnosis of disease states and corresponding herbal prescriptions.First, having used fuzzy inferences, the most seriousdisease state in which the patient appears to be infected isdetermined. Next, an herbal prescription written insuitable herbs with reasonable amounts for treating thatdisease state is given by neural networks. Finally, wedescribe an application of this model in rheumatismdiagnoses and show its evaluations

Fairness evaluating effects of prescriptions is indispensably importance in disease treatment. The
development of Intelligence Technologies nowadays makes it realizable for a moderation of subjective
prescription evaluations in medicine. In this paper, we present an application of Self-Organizing Map (SOM) to
visualization of herbal prescription effects in Oriental Medicine (OM). The application is used to estimate
influence of herbal ingredients prescribed for patients who have several concurrent diseases. Inputs to the SOM
are herbal treatment prescriptions, and an output is a map that shows disease states that can be influenced by
these prescriptions. Training data is 133 standard prescriptions with 221 popular herbs in 17 disease groups
collected from oriental medical text books by experienced doctors. First, the prescription problem in OM is
described. Next, the system structure and implementation of the proposed system are shown. Finally, the
visualization abilities of the proposed system verified by experienced doctors are presented