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.

Highlights

  • Heart failure (HF) is a chronic life-threatening condition characterized by high rates of mortality and rehospitalizations

  • The goal of this study is to introduce such biomarkers in the adverse event prediction process

  • In order to evaluate the contribution of biomarkers in the prediction of adverse events, the following experiments are made: (i) all available features are given as input, (ii) features only from sensors are employed, (iii) features only from biosensors are utilized as predictors, and (iv) features from sensors and biosensors are met

Read more

Summary

Introduction

Heart failure (HF) is a chronic life-threatening condition characterized by high rates of mortality and rehospitalizations. The European Society of Cardiology reports that 26 million people worldwide suffer from HF and 74% of them present at least one comorbidity [1]. HF is characterized by frequent re-admissions to hospital. HF accounts for 1-3% of all hospital admissions, while almost the 24% of hospitalized patients are re-hospitalized within a 30-day and the 46% within a 60-day, post discharge period. The 2-17% of patients admitted to hospital with HF die while in hospital and the 17-25% die within one year of admission [2]. The cost of HF management is driven by hospitalizations, corresponding to 1-2% of total healthcare expenditure

Objectives
Methods
Results
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.