[PDF][PDF] Generative AI Models in Time-Varying Biomedical Data: A Systematic Review

RY He, V Sarwal, X Qiu, Y Zhuang, L Zhang… - …, 2024 - s3.ca-central-1.amazonaws.com
Background: Trajectory modeling is a longstanding challenge in the application of
computational methods to healthcare. However, traditional statistical and machine learning …

MEDS-Tab: Automated tabularization and baseline methods for MEDS datasets

N Oufattole, T Bergamaschi, A Kolo, H Jeong… - arxiv preprint arxiv …, 2024 - arxiv.org
Effective, reliable, and scalable development of machine learning (ML) solutions for
structured electronic health record (EHR) data requires the ability to reliably generate high …

NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Hypocaloric Enteral Nutrition in Mechanically Ventilated Patients

M Jangda, J Patel, J Gill, P McCarthy, J Desman… - medRxiv, 2025 - medrxiv.org
Achieving adequate enteral nutrition among mechanically ventilated patients is challenging,
yet critical. We developed NutriSighT, a transformer model using learnable positional coding …

Parallel Time-Sensor Attention for Electronic Health Record Classification

R DeVries, MLZ Mendoza, O Winther - openreview.net
When working with electronic health records (EHR), it is critical for deep learning (DL)
models to achieve both high performance and explainability. Here we present the Parallel …