| Summary: | 碩士 === 國立陽明大學 === 生物醫學資訊研究所 === 99 === Medical education reform, as in the PBL, the students will learn the knowledge of the section (pathology, diagnostic procedures, prognosis, etc.), through group discussions to draw mechanism map. Although the topics discussed and the cases are variable, the criteria of diagnosis and thoughts could be extended, and ithe knowledge informatization is helpful to the structure of knowledge, preservation and re-sharing. In this study, a number of knowledge representation methods are surveyed. And using the expression of the frame approach classification knowledge, and using the expression of semantic network approach logistic thinking. So named the expression of the Semantic Web Framework model (semantic network incorporated with frame model SNFM), to construct an example of anemia.
There are many pathology causes related to anemia. In order to diagnose different type of anemia, we use several methods. For example, the morphology of RBC, MCV, MCH, and PCR determine genetic traits and electrophoresis of hemoglobin. There are still some unsolved mechanisms for us to clarify. By ontology, some medlines, criteria, and mechanism can be done in AI way. Then we can develop related resources for health-related student for evolving EBM knowledge. It can play the role as CDSS, if necessary.
Using MySQL ,Excel form, and BerkeleyDB, form the rule-based knowledge. Then program is written in Prolog in order to realize semantic network and frame-based diagnosis knowledge. The user interface is built by CGI and JQuery. And also an inference engine for us to go into the insight of different kind of anemia. Pie charts are used to explain the related result of the expert system. There are not CF in use but reveal the traits because the epidemiology varied from time to time. We can use the tool to identify some anemia can be difficult to be compared with each other, for example the anemia caused by chronic illness and anemia of Fe deficiency.
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