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Clustering neonatal and pediatric intensive care time series using k-means

Year: 2026

Type: conference-paper

Abstract: 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]

Source: German Medical Science (German Research Foundation)

DOI: 10.3205/25gmds207

Authors: Abdollahi-Sissan, Anis, Oprea, Camelia, Olivier, Lena, Schoberer, Mark, Stollenwerk, André

Institutions: RWTH Aachen University, Universitätsklinikum Aachen

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7147345542

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