1. Predicting Heart Failure patient events by exploiting saliva and breath biomarkers information
- Author
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Tripoliti, Evanthia Eleftherios, Karanasiou, Georgia Spiridon, Kalatzis, Fanis Georgios, Fotiadis, Dimitrios Ioannis, Ghimenti, Silvia, Lomonaco, Tommaso, Bellagambi, Francesca, Fuoco, Roger, Naka, Katerina Kyriakos, Bechlioulis, Aris, Scali, Maria Chiara, Errachid, Abdelhamid, Department of Biomedical Research, University of Ioannina, Department of Chemistry and Industrial Chemistry - University of Pisa, Dept Cardiol, Azienda Osped Univ Pisana - Cardiothorac & Vasc Dept, University of Pisa - Università di Pisa, Micro & Nanobiotechnologies, Institut des Sciences Analytiques (ISA), Institut de Chimie du CNRS (INC)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), This work is supported by the HEARTEN project that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 643694., IEEE, IEEE Comp Soc, Biol & Artificial Intelligence Soc, European Project: 643694,H2020,H2020-PHC-2014-single-stage,HEARTEN(2015), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
event prediction ,[SDV.BIO]Life Sciences [q-bio]/Biotechnology ,breath biomarkers ,heart failure ,[CHIM]Chemical Sciences ,data mining ,[SDV.IB.BIO]Life Sciences [q-bio]/Bioengineering/Biomaterials ,saliva biomarkers - Abstract
International audience; 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.
- Published
- 2017