Validation of machine-learning model for first-trimester prediction of pre-eclampsia using cohort from PREVAL study.

Objective: Effective first-trimester screening for pre-eclampsia (PE) can be achieved using a competing-risks model that combines risk factors from the maternal history with multiples of the median (MoM) values of biomarkers. A new model using artificial intelligence through machine-learning methods has been shown to achieve similar screening performance without the need for conversion of raw data of biomarkers into MoM. This study aimed to investigate whether this model can be used across populations without specific adaptations.

GRAMM: A new method for analysis of HLA in families.

Recently, haplo-identical transplantation with multiple HLA mismatches has become a viable option for stem cell transplants. Haplotype sharing detection requires the imputation of donor and recipient. We show that even in high-resolution typing when all alleles are known, there is a 15% error rate in haplotype phasing, and even more in low-resolution typings.

Machine-learning-based prediction of pre-eclampsia using first-trimester maternal characteristics and biomarkers.

Objective: To evaluate the accuracy of predicting the risk of developing pre-eclampsia (PE) according to first-trimester maternal demographic characteristics, medical history and biomarkers using artificial-intelligence and machine-learning methods.

Methods: The data were derived from prospective non-interventional screening for PE at 11-13 weeks' gestation at two maternity hospitals in the UK. The data were divided into three subsets.