Labour monitoring and decision support: a machine-learning-based paradigm
With a simple implementation, our approach outperforms the partograph and matches the performance of state-of-the-art supervised models, while offering superior flexibility and interpretability as a real-time monitoring and decision-support solution.
Authors
- Sergio Sanchez‐Martinez (ORCID: https://orcid.org/0000-0002-5094-0282)
- Mercedes Bonet (ORCID: https://orcid.org/0000-0001-9015-1635)
- Carlos Yagüe (ORCID: https://orcid.org/0000-0002-6086-4877)
- Mariana Nogueira (ORCID: https://orcid.org/0000-0001-5343-3422)
- Pablo-Miki Martí Castellote (ORCID: https://orcid.org/0000-0003-0252-5831)
- Bart Bijnens (ORCID: https://orcid.org/0000-0003-3130-6937)
- Mathieu De Craene (ORCID: https://orcid.org/0000-0002-5251-7197)
- Olufemi T. Oladapo (ORCID: https://orcid.org/0000-0002-3371-5892)
- Gemma Piella (ORCID: https://orcid.org/0000-0001-5236-5819)
Institutions
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Universitat Pompeu Fabra (ES)
- World Health Organization (CH)
- Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (ES)
- Philips (France) (FR)
Publication Details
- Journal
- Frontiers in Global Women s Health
- Published
- 2025-04-16
- DOI
- https://doi.org/10.3389/fgwh.2025.1368575
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00