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

Single Photon Eigen-Problem with Complex Internal Dynamics

Non

Massive traumatic myositis ossificans

Introduction. Traumatic myositis ossificans (TMO) is a rare condition, which can jeopardize athletic careers, especially in cases when a large ossification is formed. The aim of this paper is to present the results of treating a large TMO mass by application of physical procedures. Case outline. A TMO mass of large dimensions was detected in a 13-year-old competitive athlete two months after the patient sustained a direct blow to the quadriceps. Physical treatment lasted three months. The patient was able to carry out normal daily activities and recreational sports activities without limitation eight months after the initial injury. The imaging over a five-year follow-up detected a decrease in height and width of the ossification, but also a discrete increase in its length. Conclusion. Physical therapy resulted in full functional recovery, regardless of the massive ossification. The height and width of the ossification was reduced, but its length increased