Automatic Mobile Diagnostic System for Collaborative Patients Based on Data Migration and Users Experiences

摘要

The knowledge extraction is an interactive and iterative process of analyzing a mass of raw data with the aim of extracting distributed knowledge, usable and adaptable to a given situation and profile. In the perspectives of implementing a medical knowledge extraction system, we present in this paper the study, design and development of an incremental knowledge base for medical diagnosis based on decision trees. The knowledge results from shared experiences, by connected users to the system, produce a base of incremental knowledge improved gradually for every new experience of various users. We are interested in the construction process of knowledge allowing the exchange and the adaptation of the shared results according to the user profile. Our solution includes an automatic verification system to the knowledge base in order to validate user experiences. It also helps to promote the most profitable and reliable experiences, giving users more choices and options for a safe operating procedures.