Improving Readability of Medical Data by Using Decision Rules

As medical journal abstracts have become more and more difficult to read, there is a burning issue for doctors to get relevant medical information in order to solve a problem in a fast and efficient way. The paper deals with the synthesis of sentences of spoken language from tabular historical data that relate to a specific medical sub domain. In this case Systematic Syntax Classification of\ud Objects or SSCO algorithm was used in order to generate decision rules which were consequently transformed to natural language and delivered to the user by a machine text reader. The system is “hands–free”, reliable, and enables communication by natural language. The experiments were conducted on data sample consisting of patient’s conditions after hip surgery procedure and originating from General hospital “Djordje Joanovic”, Zrenjanin, Serbia