Enhancing Clinical Decision-Making in Pediatric Monitoring: Learning Threshold Alarm Patterns to Predict Critical Illness
The analysis of threshold alarm data before critical illness events has provided valuable insights into threshold alarm patterns associated with death and sepsis. The data revealed distinct patterns in ECGRR, ECGHR, and SPO2 signals, and most of the threshold alarms were in the lower duration. The random forest classifier effectively distinguished these learned patterns around death and sepsis events compared to other algorithms. Further studies are required on the use of algorithms on all vital sign signal features in clinical settings.
Authors
- Bart Bierling (ORCID: https://orcid.org/0000-0001-5257-8558)
- Mphatso Kamndaya (ORCID: https://orcid.org/0000-0002-7597-3339)
- Job C. J. Calis
- Patrick Phepa
- Christina Chiziwa (ORCID: https://orcid.org/0009-0009-6893-1198)
- IMPALA Project Team
Institutions
- University of Malawi (MW)
- Malawi University of Science and Technology (MW)
- Kamuzu Central Hospital (MW)
- Emma Kinderziekenhuis (NL)
- Amsterdam Institute for Global Health and Development (NL)
- Robert Bosch (Netherlands) (NL)
Publication Details
- Journal
- Bioengineering
- Published
- 2025-11-05
- DOI
- https://doi.org/10.3390/bioengineering12111210
- Primary Topic
- Healthcare Technology and Patient Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- European and Developing Countries Clinical Trials Partnership