Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models
Performance of our expectant and medical miscarriage management ML models demonstrate consistency across validation and external test sets. These ML methods, validated and externally tested, have the potential to offer personalised prediction outcome of expectant and medical management of miscarriage.
Authors
- M. Pikovsky (ORCID: https://orcid.org/0000-0003-2538-5851)
- Nina Parker
- Tom Bourne (ORCID: https://orcid.org/0000-0003-1421-6059)
- Srdjan Saso (ORCID: https://orcid.org/0000-0002-6310-032X)
- Sughashini Murugesu (ORCID: https://orcid.org/0000-0001-8852-0892)
- C. Stalder
- M. Al‐Memar (ORCID: https://orcid.org/0000-0002-3662-3010)
- E. Braun
- Jennifer Frances Barcroft (ORCID: https://orcid.org/0000-0001-5258-312X)
- Nina Cooper (ORCID: https://orcid.org/0000-0002-5828-8014)
- Alex Novak (ORCID: https://orcid.org/0000-0002-5880-8235)
- Kristofer Linton‐Reid (ORCID: https://orcid.org/0000-0003-4723-7198)
- Eric O. Aboagye (ORCID: https://orcid.org/0000-0003-2276-6771)
Institutions
- Queen Charlotte's and Chelsea Hospital (GB)
- Imperial College London (GB)
- KU Leuven (BE)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2025-02-28
- DOI
- https://doi.org/10.1186/s12884-025-07283-y
- Citations
- 5
- Primary Topic
- Ectopic Pregnancy Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 9.39
Funders
- Imperial Health Charity