Software Architecture of a Learning Apprentice System in Medical Billing
Ahmed Aftab, Umair Abdullah, Mohammad Jamil Sawar · 2010
Machine learning is an emerging field of computer science concerned with the learning of knowledge from exploration of already stored data. However, effective utilization of extracted knowledge is an important issue. Extracted knowledge may be best utilized via feeding to knowledge based system. To this end, the work reported in this paper is based on a novel idea to enhance the productivity of the previously developed systems. This paper presents the proposed architecture of a Learning Apprentice System in Medical Billing system being developed for medical claim processing. A new dimension is added whereby, the process of extracting and utilization of knowledge are implemented in relational database environment for improved performance. It opens enormous application areas as most business data is in relational format managed by some relational database management server. The major components of the proposed system include knowledge base, rule engine, knowledge editor, and data mining module. Knowledge base consists of rules, meta rules and logical variables defined in the form of SQL queries stored in relational tables. Rule engine has been successfully developed and deployed in the form of SQL stored procedures. Knowledge editor and data mining modules are under development. Given architecture depicts over all business process of medical billing along with major components of the system. The proposed architecture effectively integrates all three pertinent components given by data mining (production rule discovery), rule based systems technology and database systems environment.