Analysis of pathogenesis of common diseases in medical records of gangrene cases and characteristics of their diagnosis and treatment
In this study,the prescriptions for oral administration in the ancient and modern medical records of gangrene cases were subjected to frequency statistics and association rule analysis by modern statistical methods and data mining techniques.This study aims to investigate the characteristics of combined medication for common gangrene diseases and to reveal the pathogenesis of gangrene diseases and the characteristics of their diagnosis and treatment,thus providing a basis for disease differentiation-based treatment of gangrene.
A challenge of future knowledge management and decision support systems is to combine the storage and effective reuse of data, systematically captured as process or system information, with user experience in dealing with problems and non-trivial situations. In CBR, situation-specific user experiences are typically captured in cases. In our approach, cases are linked within a semantic network of more general domain knowledge. In this paper we present a way to automate the construction and dynamical refinement of such a model of case-specific and general knowledge, on the basis of external process data continuously being generated. A data mining method based on a Bayesian Networks approach is used. We are also looking into how the notion of causality, being a central issue in both BNs and model-based AI, can be compared and better understood by relating it to such a combined model.