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[PDF][PDF] Generative AI Models in Time-Varying Biomedical Data: A Systematic Review
Background: Trajectory modeling is a longstanding challenge in the application of
computational methods to healthcare. However, traditional statistical and machine learning …
computational methods to healthcare. However, traditional statistical and machine learning …
MEDS-Tab: Automated tabularization and baseline methods for MEDS datasets
Effective, reliable, and scalable development of machine learning (ML) solutions for
structured electronic health record (EHR) data requires the ability to reliably generate high …
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
Achieving adequate enteral nutrition among mechanically ventilated patients is challenging,
yet critical. We developed NutriSighT, a transformer model using learnable positional coding …
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 …
models to achieve both high performance and explainability. Here we present the Parallel …