Integration of clinical features in a computerized cardiotocography system to predict severe newborn acidemia
The CTG features have a good accuracy to predict severe newborn acidemia, confirming existing literature. Integrating clinical features tends to enhance the accuracy. Further research will aim at using more advanced machine learning models to combine the features more efficiently.
Authors
- Jade Merrer (ORCID: https://orcid.org/0000-0002-6379-6953)
- J. Stirnemann
- Emilia Holmström (ORCID: https://orcid.org/0000-0002-2841-2731)
- Inesse Ait Amara
- Elena Menzhulina
- Juliette Vitrou
- Erwan Le Pennec
- Imane Ben M' Barek
Institutions
- Délégation Paris 5 (FR)
- Centre National de la Recherche Scientifique (FR)
- École Polytechnique (FR)
- Inserm (FR)
- Université Paris Cité (FR)
- Hôpital Beaujon (FR)
- Assistance Publique – Hôpitaux de Paris (FR)
- Hôpital Robert-Debré (FR)
Publication Details
- Journal
- European Journal of Obstetrics & Gynecology and Reproductive Biology
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1016/j.ejogrb.2025.01.030
- Citations
- 3
- Primary Topic
- Neonatal and fetal brain pathology
- Type
- article
- Field-Weighted Citation Impact
- 10.48
Funders
- Assistance publique-Hôpitaux de Paris