A5000945353- Pregnancy and preeclampsia studies
- Birth, Development, and Health
- Renal Transplantation Outcomes and Treatments
- T-cell and B-cell Immunology
- Maternal and fetal healthcare
- Cytomegalovirus and herpesvirus research
- Genetic Associations and Epidemiology
- Reproductive System and Pregnancy
- Gestational Diabetes Research and Management
Bar-Ilan University
2022-2023
Areté Associates (United States)
2023
ABSTRACT Objective To evaluate the accuracy of predicting risk developing pre‐eclampsia (PE) according to first‐trimester maternal demographic characteristics, medical history and biomarkers using artificial‐intelligence machine‐learning methods. Methods The data were derived from prospective non‐interventional screening for PE at 11–13 weeks' gestation two maternity hospitals in UK. divided into three subsets. first set, including 30 437 subjects, was used develop training process, second...
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 median (MoM) values biomarkers. A new artificial intelligence through machine-learning methods has been shown to achieve similar performance without need conversion raw data biomarkers into MoM. This study aimed investigate whether this used across populations specific adaptations.
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 15% error rate haplotype phasing, more low‐resolution typings. Similarly, related donors, parents' haplotypes should be imputed to determine what each child inherited. propose graph‐based family (GRAMM) phase...
The objective of this study was to validate an artificial intelligence (AI) model that predicts the risk pre-eclampsia (PE) based on maternal demographic, medical and pregnancy history, use first trimester biomarkers. We used a previously trained machine learning predicted early (< 34 weeks gestation) preterm 37 PE versus no using data set from two UK hospitals. demographic characteristics, applied Spanish for external validation, scaling biomarker values consistency. measured accuracy area...
Recently, haplo-identical transplantation with multiple HLA mismatches has become a viable option for system cell transplants. Haplotype sharing detection requires imputation of donor and recipient. We show that even in high-resolution typing when all alleles are known, there is 15% error rate haplotype phasing, more low resolution typings. Similarly, related donors, parents haplotypes should be imputed to determine what each child inherited. propose GRAMM (GRaph bAsed FaMilly iMputation)...