Research on Development of Pathological Knowledge Management Platform

Pathology is a fundamental discipline of basic medicine, and pathological diagnosis is the "golden standard" in clinical medicine, serving as the basis for clinical doctors to formulate treatment plans. Pathological knowledge can be divided into explicit pathological knowledge, which is present in materials, and tacit pathological knowledge, which exists in pathologists' minds. Due to the lack of comprehensive mechanisms and methods, the transformation between explicit and tacit pathological knowledge encounters some obstacles, making it difficult for valuable pathological knowledge to be disseminated and shared, resulting in significant knowledge waste. This research develops a pathological knowledge management platform using the SSM framework based on Java language, constructing a comprehensive pathological knowledge platform including five subsystems: pathological knowledge network map, online pathology learning, remote pathology consultation, pathological knowledge review, pathological knowledge sharing and communication. The platform provides flexible learning methods for pathological knowledge, enhances the interactivity, realizes efficient sharing of pathological knowledge, promotes the professional capabilities of pathologists and residents, and improves the efficiency and accuracy of pathological diagnosis. Meanwhile, the platform utilizes date mining techniques such as clustering analysis, association analysis to discover hidden patterns and rules buried in large amount of pathology data. The mined results can be applied for clinical auxiliary diagnosis and prediction, therefore improves the cure rate, as well as the health level of residents.