arXiv Machine Learning By Jeffrey D. Varner, Maria Cristina Bravo, Carole McBride, Thomas Orfeo, Ira Bernstein

Validated Synthetic Patient Generation for Small Longitudinal Cohorts: Coagulation Dynamics Across Pregnancy

Read the original on arXiv Machine Learning →

arXiv:2604. 07557v2 Announce Type: replace Abstract: Small longitudinal cohorts, common in maternal health, rare diseases, and early-phase trials, limit computational modeling because enrollment is slow and the data are too sparse to train reliable models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jul 21

Differentiable latent structure discovery for interpretable forecasting in clinical time series

arXiv:2604. 27967v2 Announce Type: replace Abstract: Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.

By Ivan Lerner, Jean Feydy, Alexandre Kalimouttou, Anita Burgun, Francis Bach
arXiv Machine Learning
Oct 16

Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble

The study presents a curated dataset of 616 fresh embryo-transfer cycles and develops a Hybrid Attention Network (HAN) combined with logistic regression to predict live birth after IVF/ICSI. The HAN+LR ensemble achieved the best performance with an AUC‑ROC of 0.710, outperforming both individual components and other machine‑learning baselines. The differentially weighted trophectoderm morphology emerged as the most important predictor across models.

By Abdul Raheem Shahzad, Jinxin Chen, Ming Zhang, Juan Deng