Machine Learning for the Prediction of Surgical Morbidity in Placenta Accreta Spectrum
· ML models are useful models are useful in predicting PAS-related surgical morbidity.. · Optimal management for PAS remains unclear.. · Utilizing ML can serve as a foundation for risk and complexity stratification in cases of PAS..
Authors
- Itamar Futterman (ORCID: https://orcid.org/0000-0002-6766-3437)
- Myah Griffin (ORCID: https://orcid.org/0000-0002-7215-2965)
- P. Dar (ORCID: https://orcid.org/0000-0002-9254-4261)
- Georgios Doulaveris (ORCID: https://orcid.org/0000-0001-5574-235X)
- Meghana Limaye
- Lois Brustman (ORCID: https://orcid.org/0000-0001-5311-1984)
- Henri M. Rosenberg
- Rebecca H. Jessel (ORCID: https://orcid.org/0000-0002-8146-1137)
- Thomas Owens (ORCID: https://orcid.org/0000-0002-6673-3413)
- Alexa Cohen (ORCID: https://orcid.org/0000-0003-2430-1062)
- Chaskin Wells Saroff
- Olivia Sher
- Scott Chudnoff
- Shoshana Haberman
Institutions
- Albert Einstein College of Medicine (US)
- Genomatica (United States) (US)
- Maimonides Medical Center (US)
- Montefiore Medical Center (US)
- NYU Langone Health (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- American Journal of Perinatology
- Published
- 2024-09-17
- DOI
- https://doi.org/10.1055/a-2405-3459
- Citations
- 4
- Primary Topic
- Maternal and fetal healthcare
- Type
- article
- Field-Weighted Citation Impact
- 4.44