Published in

Hindawi, Computational and Mathematical Methods in Medicine, (2016), p. 1-10, 2016

DOI: 10.1155/2016/8748156

Links

Tools

Export citation

Search in Google Scholar

Computer Aided Detection System for Prediction of the Malaise during Hemodialysis

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Green circle
Published version: archiving allowed
Data provided by SHERPA/RoMEO

Abstract

Monitoring of dialysis sessions is crucial as different stress factors can yield suffering or critical situations. Specialized personnel is usually required for the administration of this medical treatment; nevertheless, subjects whose clinical status can be considered stable require different monitoring strategies when compared with subjects with critical clinical conditions. In this case domiciliary treatment or monitoring can substantially improve the quality of life of patients undergoing dialysis. In this work, we present aComputer Aided Detection(CAD) system for the telemonitoring of patients’ clinical parameters. The CAD was mainly designed to predict the insurgence of critical events; it consisted of twoRandom Forest(RF) classifiers: the first one (RF1) predicting the onset of any malaise one hour after the treatment start and the second one (RF2) again two hours later. The developed system shows an accurate classification performance in terms of bothsensitivityandspecificity. Thespecificityin the identification of nonsymptomatic sessions and thesensitivityin the identification of symptomatic sessions forRF2are equal to 86.60% and 71.40%, respectively, thus suggesting the CAD as an effective tool to support expert nephrologists in telemonitoring the patients.