Author

Wan-Su Chen

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Open access Sep 2026

Rethinking input complexity in transformer-based clinical prediction: implications for feature dimensionality and sequence length in longitudinal electronic health record data

Abstract Objectives Transformer-based models for clinical prediction using longitudinal electronic health record (EHR) data are often developed with large feature sets and long patient histories under the assumption that more data improves performance. However, high-dimensional inputs and long sequences increase comput...

Wan-Su Chen, Bo-Tao Zhou, R. Zeiger et al. · JAMIA Open · 0 citations

Open access Sep 2026

Rethinking Input Complexity in Transformer-Based Clinical Prediction: Implications for Feature Dimensionality and Sequence Length in Longitudinal EHR Data

Transformer-based prediction models maintained strong performance across reduced feature sets, while dimensionality reduction modestly affected calibration at the highest risk levels, moderate sequence-length reduction substantially reduced computational burden with limited change in overall discrimination.

Wan-Su Chen, Bo-Tao Zhou, R. Zeiger et al. · medRxiv · 0 citations