Predicting Heart Failure patient events by exploiting saliva and breath biomarkers information

Abstract : The aim of this work is to present a machine learning based method for the prediction of adverse events (mortality and relapses) in patients with heart failure (HF) by exploiting, for the first time, measurements of breath and saliva biomarkers (Tumor Necrosis Factor Alpha, Cortisol and Acetone). Data from 27 patients are used in the study and the prediction of adverse events is achieved with high accuracy (77%) using the Rotation Forest algorithm. As in the near future, biomarkers can be measured at home, together with other physiological data, the accurate prediction of adverse events on the basis of home based measurements can revolutionize HF management.
Type de document :
Direction d'ouvrage, Proceedings, Dossier
IEEE. 17th IEEE International Conference on Bioinformatics and Bioengineering (BIBE), Oct 2017, Herndon, VA, United States. pp.285-290, 2017, 2017 IEEE - 17th IEEE International Conference on Bioinformatics and Bioengineering (BIBE), 978-1-5386-1324-5. 〈10.1109/BIBE.2017.00055〉
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01859469
Contributeur : Agnès Bussy <>
Soumis le : mercredi 22 août 2018 - 10:12:33
Dernière modification le : jeudi 30 août 2018 - 09:09:02

Identifiants

Collections

Citation

Evanthia Eleftherios Tripoliti, Georgia Spiridon Karanasiou, Fanis Georgios Kalatzis, Dimitrios Ioannis Fotiadis, Silvia Ghimenti, et al.. Predicting Heart Failure patient events by exploiting saliva and breath biomarkers information . IEEE. 17th IEEE International Conference on Bioinformatics and Bioengineering (BIBE), Oct 2017, Herndon, VA, United States. pp.285-290, 2017, 2017 IEEE - 17th IEEE International Conference on Bioinformatics and Bioengineering (BIBE), 978-1-5386-1324-5. 〈10.1109/BIBE.2017.00055〉. 〈hal-01859469〉

Partager

Métriques

Consultations de la notice

23