Selecting Relevant Information for Medical Decision Support with Application in Cardiology

0395155 - ÚI 2014 RIV CZ eng J - Journal Article
Kalina, Jan - Seidl, L. - Grünfeldová, H. - Slovák, Dalibor - Zvárová, Jana
Selecting Relevant Information for Medical Decision Support with Application in Cardiology.
European Journal for Biomedical Informatics. Roč. 9, č. 1 (2013), s. 2-6. ISSN 1801-5603
R&D Projects: GA MŠMT(CZ) 1M06014
Institutional support: RVO:67985807
Keywords : decision support system * web-service * information extraction * high dimension * gene expressions
Subject RIV: IN - Informatics, Computer Science

Objectives: The aim of our work was to implement a prototype of a decision support system which has the form of a web-based classification service. Because the data analysis component of decision support systems often happens to be unsuitable for high-dimensional data, special attention must be paid to the sophisticated selection of the most relevant variables before learning the classification rule. Methods: We implemented a prototype of a diagnostic decision support system called SIR. The system has the ability to select the most relevant variables based on a set of high-dimensional measurements by means of a forward procedure optimizing a decision-making criterion. This allows to learn a reliable classification rule. Results: The implemented prototype was tested on a sample of patients involved in a cardiology study. We used SIR to perform an information extraction from a cardiological clinical study containing both clinical and gene expression data. The classification performance was evaluated by means of a cross validation study. Conclusions: The proposed classification system can be useful for clinicians in primary care to support their decision-making tasks with relevant information extracted from any available clinical study. It is especially suitable for analyzing high-dimensional data, e.g. gene expression measurements.
Permanent Link: http://hdl.handle.net/11104/0223270