Introduction: Machine learning models have shown promising potential in individual-level outcome prediction for patients with psychosis, but also have several limitations. To address some of these limitations, we present a model that predicts multiple outcomes, based on longitudinal patient data, while integrating prediction uncertainty to facilitate more reliable clinical decision-making.
Material And Methods: We devised a recurrent neural network architecture incorporating long short-term memory (LSTM) units to facilitate outcome prediction by leveraging multimodal baseline variables and clinical data collected at multiple time points.
Background: Incidentally, the non-invasive prenatal test (NIPT) shows chromosomal aberrations suspicious of a maternal malignancy, especially after genome-wide testing. The aim of this study is to determine how many cases of cancer in pregnancy are diagnosed or missed with NIPT and whether in retrospect subtle changes in NIPT results could have detected cancer.
Methods: We identified Dutch patients diagnosed in 2017-2021 with pregnancy-associated cancer from the International Network on Cancer, Infertility and Pregnancy (INCIP) Registry, who underwent NIPT in the Dutch NIPT implementation study (TRIDENT-2).
Objective: Non-invasive prenatal testing (NIPT) investigates placental DNA and may detect confined placental mosaicism (CPM). The aim of this study was to confirm CPM in the term placenta in cases with abnormal NIPT but normal follow-up cytogenetic studies of fetus and mother. Additionally we examined the distribution of abnormal cells over the placenta.
View Article and Find Full Text PDFObjective: To evaluate the diagnostic yield of exome sequencing (ES) in fetuses and neonates with prenatally detected congenital diaphragmatic hernia (CDH) and normal copy number variant (CNV) analysis.
Methods: We conducted a retrospective cohort study of prenatally diagnosed CDH cases seen between 2019 and 2022. All cases who underwent prenatal or postnatal genetic testing were reviewed.