Semantically interoperable research medical data and meta data extraction strategy

Abstract

Generally, the interoperability is the key feature to any widely used application or an information system. A system build on the basis of semantic data interpretation can also lead to an easy extensible and adaptable system, in the future. Otherwise, you have to deal with a lack of agreed terminologies or codes between standards e.g. DASTA vs. HL7, different coding structures and even variety of file formats or inconsistent database table schema. The most of these difficulties can be solved by the semantically interoperable system. We present our implementation strategy and meta data extraction methods for a research information system with an heterogeneous medical data. The medical data can have different origin, type, file format and even its version. We discuss the research information system that we primarily use for cerebrovascular brain diseases research.

MedIDEA — Medical image data extraction and analysis

“…Our proposed algorithm serves as part of an expert system described in [1] and [2] that allows medical doctors reconstruct 3D models of the required organs. The knowledge base is created by computed tomography images analysis used for automatic visual and/or property similar images and patients search.…”

Unified Parallel Experiment Interface for Medical Research System

“…The aim of this article is demonstration of unified parallel interface for implementation medical experiments. General design of our research information system will be presented in (Vcelak and Kleckova, 2011). There is some interoperability issues between experiments, which we need to solve during development.…”