A Dermatopathological Diagnostic Decision Support System for Non-Infectious Generalized Blistering Diseases

• Main Authors: Horng-Sheng Shiue, 薛宏昇
• Other Authors: Yu-Chuan Li
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/26579808176687852766

碩士 === 台北醫學院 === 醫學資訊研究所 === 88 === Abstract In recent decades of "information explosion", it is difficult for practicing physicians to keep up to date with all the information for making optimal clinical judgements. The aim of this project tried to build a dermatopathological decision support system to help physicians to make optimal clinical decisions. In lights of the uncertainties involved in the dermatopathological reasoning process, we proposed a probabilistic knowledge representation under the scope of "non-infectious generalized blistering diseases". In order to create the knowledge base, a technique termed “knowledge engineering” was implemented. This knowledge engineering process included one dermatopathological expert, one coordinator and one knowledge engineer. This group met every two weeks during a six-month period to build up a data dictionary and knowledge base. Programming engineer developed the inference engine and user interface in a web-based architecture. Vigorous evaluation process was performed after all parts of the system were put together. Both theoretical performance indices and clinical performance are to be tested. Published CPC (clinical-pathological conference) cases and patients charts are used for the final evaluation. This prototype knowledge base represents 11 disease frames with 88 findings in the data dictionary. Each disease frame consists of 5~10 findings, which represents clinical, light microscopic and immunopathologic clues for the diagnosis of a disease. There are several advantages with the web-based system shell and the user interface. First, multiple users in different geographical areas can update their knowledge base in the same time, so the international cooperation is feasible. Second, those who like to use the system need only a Internet access to do so. No additional software needs to be sent or installed. Third, multiple knowledge bases in different medical domains can be incorporated into the system for cross-domain diagnostic decision support and clinical teaching. This project proposes to apply probabilistic reasoning to a web-based dermatopathological diagnostic decision support system. The system can be useful in both clinical teaching and diagnostic decision support. In the process of knowledge engineering, we also felt that the domain medical knowledge was better clarified and may contribute to the future of evidence-based medicine in dermatology.