| Co-Investigator(Kenkyū-buntansha) |
SATO Toshirou School of Medicine, Kitasato University, 医学部, 教授 (60050366)
KAMATA Takenobu Osaka University Medical School, 医学部, 教授 (80028399)
KAIHARA Shigekoto Faculty of Medicine, University of Tokyo, 医学部, 教授 (80084515)
岡島 光治 藤田学園保健衛生大学, 医学部, 教授
IIO Masahiro Faculty of Medicine, University of Tokyo, 医学部, 教授 (80143486)
OKAJIMA Mitsuharu Fujita-Gakuen Health University, School of Medicine
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| Research Abstract |
The aims of this study is to formulate and analyze the diagnostic thinking processes of physician using by different working hypothesis. Based on an appeal for contributions of specialists who have been studying in the field of computer applications in clinical medicine, two research paradigms were picked up as the most important keys from the soft-ware side. One is knowledge engineering, and another one is inverse solution. In addition, the rapid extension of image diagnosis technologies was re-evaluated. Knowledge engineering theories and technologies have been establishing the practical value as the expert consultstion systems in clinical diagnosis. Some of these systems were confirmed the even ability comparing to those of physician, but in each extremely specialized problems,such as general history taking aid, electrocardiogram interpretation, calorie consultation, fluid therapy diagnosis, etc. On the other hand, the application of inverse solution was also fixed its credit in the case of computerized X-ray tomography, and was expanding into the indirect estimation of anatomical and functional characteristics of organs and tissues through magnetic resonance images,ultrasoundimages, thermographic images, microwave images and so on. However, none of specialist was satisfied by one of individual theory and technology, and anyone expected the benefits of unrestricted and composite applications of traditional working hypothesis or models, including stochastic analysis, multivariate statistical analysis, simulation models, logic algebras, decision-making trees, etc. The role of knowledge engineering shuld be to standardize, normalize, coordinate, and unify the scatteringly different paradigms. Another important strategical subgoal of knowledge engineering should be the improvement of interpretation and processing of image diagnosis.
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