By Pooja Toshniwal PahariaAug 21 2023Reviewed by Lily Ramsey, LLM In a recent study posted to the medRxiv* preprint server, researchers developed a machine-learning-based model using the Medical Record Longitudinal Information AI System platform for longitudinal preeclampsia risk estimation.
Proteinuria, increased liver enzymes, lung edema, convulsions, and death can all occur due to increasing end-organ damage. The study included a large group of women who gave birth at two hospitals for tertiary care and six centers for community care in New England between February 2015 and June 2023. The training and testing datasets comprised 80% and 20% of cases, respectively. The team performed a 5.0-fold cross-validation over the training set to ascertain hyperparameters, following which the most appropriate combination was used to determine the testing metrics.
In addition, using the xgboost model predictions at 14.0 weeks on the testing dataset, 25% individuals would be eligible for aspirin prophylaxis, whereas 15% would be eligible using the American College of Obstetricians and Gynecologists criteria. Additionally, preeclampsia patients had an increased likelihood of familial hypertension, higher maximal diastolic and systolic blood pressures, and higher weight gain during pregnancy than non-preeclampsia individuals.