Forecasting Thresholds Alarms in Medical Patient Monitors using Time Series Models

Jonas Chromik, Bjarne Pfitzner, Nina Ihde, Marius Michaelis, Denise Schmidt, Sophie Anne Ines Klopfenstein, Akira-Sebastian Poncette, Felix Balzer, Bert Arnrich

2022

Abstract

Too many alarms are a persistent problem in today’s intensive care medicine leading to alarm desensitisation and alarm fatigue. This puts patients and staff at risk. We propose a forecasting strategy for threshold alarms in patient monitors in order to replace alarms that are actionable right now with scheduled tasks in an attempt to remove the urgency from the situation. Therefore, we employ both statistical and machine learning models for time series forecasting and apply these models to vital parameter data such as blood pressure, heart rate, and oxygen saturation. The results are promising, although impaired by low and non-constant sampling frequencies of the time series data in use. The combination of a GRU model with medium-resampled data shows the best performance for most types of alarms. However, higher time resolution and constant sampling frequencies are needed in order to meaningfully evaluate our approach.

Paper Citation

in Harvard Style

Chromik J., Pfitzner B., Ihde N., Michaelis M., Schmidt D., Klopfenstein S., Poncette A., Balzer F. and Arnrich B. (2022). Forecasting Thresholds Alarms in Medical Patient Monitors using Time Series Models. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF; ISBN 978-989-758-552-4, SciTePress, pages 26-34. DOI: 10.5220/0010767300003123

in Bibtex Style

@conference{healthinf22,
author={Jonas Chromik and Bjarne Pfitzner and Nina Ihde and Marius Michaelis and Denise Schmidt and Sophie Anne Ines Klopfenstein and Akira-Sebastian Poncette and Felix Balzer and Bert Arnrich},
title={Forecasting Thresholds Alarms in Medical Patient Monitors using Time Series Models},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF},
year={2022},
pages={26-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010767300003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF
TI - Forecasting Thresholds Alarms in Medical Patient Monitors using Time Series Models
SN - 978-989-758-552-4
AU - Chromik J.
AU - Pfitzner B.
AU - Ihde N.
AU - Michaelis M.
AU - Schmidt D.
AU - Klopfenstein S.
AU - Poncette A.
AU - Balzer F.
AU - Arnrich B.
PY - 2022
SP - 26
EP - 34
DO - 10.5220/0010767300003123
PB - SciTePress