SP 1.2.1 - Machine Learning for longitudinal data analysis in gas sensors: A case study in early subclinical ketosis in dairy cows
- Event
- EUROSENSORS 2026
2026-09-06 - 2026-09-09
Zurich - Band
- Lectures
- Chapter
- Antimicrobial Resistance Diagnostics
- Author(s)
- A. Belenguer-Llorens, C. R. Jutzeler - ETH Zurich,Zurich (Switzerland) and SIB Swiss Institute of Bioinformatics,Lausanne (Switzerland), J. Hendricks, A. -M. Reiche, F. Dohme-Meier - Agroscope,Posieux (Switzerland), J. Ko, A. T. Güntner - ETH Zurich,Zurich (Switzerland)
- Pages
- 283 - 284
- DOI
- 10.5162/eurosensors2026/SP1.2.1
- ISBN
- 978-3-910600-12-6
- Price
- free
Abstract
Machine learning offers powerful opportunities to transform continuous sensor measurements into clinically or biologically predictions. However, translating raw sensor signals into robust diagnostic models requires careful consideration of temporal dynamics, feature engineering, latent-state modeling, and interpretability. In this tutorial, we present a generalizable pipeline for developing predictive diagnostic models from sensor measurements, illustrated through a real-world case study: the early detection of subclinical ketosis in dairy cows using breath acetone measured by gas sensors.