Clustering neonatal and pediatric intensive care time series using k-means
Introduction: Continuous recordings of different sensors in clinical settings, such as neonatal and pediatric intensive care units (NICU/PICU), produce large volumes of unlabeled multivariate time series (MTS) data. Labeling data is time-intensive, requires trained staff, and is error-prone. Unsupervised [for full text, please go to the a.m. URL]
Authors
- Camelia Oprea
- Lena Olivier (ORCID: https://orcid.org/0000-0003-0169-4510)
- Mark Schoberer (ORCID: https://orcid.org/0000-0002-6827-1849)
- André Stollenwerk
- Anis Abdollahi-Sissan
Institutions
- RWTH Aachen University (DE)
Publication Details
- Journal
- German Medical Science (German Research Foundation)
- Published
- 2026-04-01
- DOI
- https://doi.org/10.3205/25gmds207
- Primary Topic
- Time Series Analysis and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.00