Self organizing neural network approach for identification of patients with Congestive Heart Failure

Elfadil, Nazar; Ibrahim, Intisar

doi:10.1109/icmcs.2011.5945658

Cited by 10 publications

(2 citation statements)

References 2 publications

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“…Lack of adherence is common, resulting to destabilizations, re-hospitalizations and adverse events including death [1]. Towards this direction, several studies focusing on HF management have been presented in the literature, either based on machine learning approaches which address (separately or in combination) early diagnosis of HF [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], HF subtype recognition [19][20][21], severity estimation [22][23][24][25][26][27][28], prediction of adverse events [24,[29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]…”

Section: Introductionmentioning

confidence: 99%

“…In most of the studies the diagnosis of HF has been achieved mainly by utilizing only heart rate variability (HRV) measures [2][3][4][5][6][7][8][9][10][11][12][13], while some studies combine HRV measures with anamnestic and instrumental data [14][15][16][17][18]. The above mentioned data are given as input to different classifiers, such as Support Vector Machines, Classification and Regression Trees, Random Forests, etc..…”

Section: Introductionmentioning

confidence: 99%

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HEARTEN KMS – A knowledge management system targeting the management of patients with heart failure

Tripoliti

Karanasiou

Kalatzis

et al. 2019

Journal of Biomedical Informatics

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The aim of this work is to present the HEARTEN Knowledge Management System, one of the core modules of the HEARTEN platform. The HEARTEN platform is an mHealth collaborative environment enabling the Heart Failure patients to self-manage the disease and remain adherent, while allowing the other ecosystem actors (healthcare professionals, caregivers, nutritionists, physical activity experts, psychologists) to monitor the patient's health progress and offer personalized, predictive and preventive disease management. The HEARTEN Knowledge Management System is a tool which provides multiple functionalities to the ecosystem actors for the assessment of the patient's condition, the estimation of the patient's adherence, the prediction of potential adverse events, the calculation of Heart Failure related scores, the extraction of statistics, the association of patient clinical and non-clinical data and the provision of alerts and suggestions. The innovation of this tool lays in the analysis of multiparametric personal data coming from different sources, including for the first time breath and saliva biomarkers, and the use of machine learning techniques. The HEARTEN Knowledge Management System consists of nine modules. The accuracy of the KMS modules ranges from 78-95% depending on the module/functionality.

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