Computer Aided Detection System for Prediction of the Malaise during Hemodialysis

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 a Computer 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 two Random 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 both sensitivity and specificity. The specificity in the identification of nonsymptomatic sessions and the sensitivity in the identification of symptomatic sessions for RF2 are equal to 86.60% and 71.40%, respectively, thus suggesting the CAD as an effective tool to support expert nephrologists in telemonitoring the patients.

Cost-effectiveness of home telemonitoring in chronic kidney disease patients at different stages by a pragmatic randomized controlled trial (eNephro): rationale and study design

“…Furthermore, knowing one is under telesurveillance and that important health data can be transmitted quickly is reassuring. For patients on haemodialysis, the transmission of data issuing from the generator during the dialysis session improves the detection of problems involving a malaise [31] or the vascular access [18]. Transplant recipients should also benefit from telesurveillance facilitated by a specifically designed expert system [12], and the number of visits to the transplantation centre may be limited when clinical and biological parameters remain within target ranges.…”

Improved Survival Analyses Based on Characterized Time-Dependent Covariates to Predict Individual Chronic Kidney Disease Progression

“…The prediction of CKD progression is an important task for patient care in clinical management. Machine learning (ML) methods have been used to predict the risk of CKD applications in recent years [ 15 , 16 , 17 , 18 ]. In addition, several risk prediction models have been proposed for CKD applications [ 14 , 19 , 20 , 21 , 22 ].…”