Prediction of pre-eclampsia with machine learning approaches: Leveraging important information from routinely collected data
Two ML models created the highest-performing prediction using routinely collected data to identify women at high risk of PE, with acceptable discrimination. However, to confirm this result and also examine model generalisability, external validation studies are needed in other settings, utilising standardised prognostic factors.
Authors
- Daniel L. Rolnik (ORCID: https://orcid.org/0000-0002-2263-3592)
- Helena Jane Teede (ORCID: https://orcid.org/0000-0001-7609-577X)
- Joanne Enticott (ORCID: https://orcid.org/0000-0002-4480-5690)
- Sofonyas Abebaw Tiruneh (ORCID: https://orcid.org/0000-0001-5867-5105)
Institutions
- Monash Health (AU)
- Monash University (AU)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2024-10-05
- DOI
- https://doi.org/10.1016/j.ijmedinf.2024.105645
- Citations
- 13
- Primary Topic
- Pregnancy and preeclampsia studies
- Type
- article
- Field-Weighted Citation Impact
- 7.58