Author

E. Puttock

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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

Implementation of risk prediction models using electronic health data for early detection of pancreatic cancer: a prospective pilot feasibility study

Background & AimsWe previously developed multiple machine-learning and regression-based risk prediction models using retrospective electronic health records (EHR) to estimate near-term risk of pancreatic cancer (PC). This pilot study aimed to assess the feasibility of an early detection strategy combining initial EHR-b...

Bechien U. Wu, T. Luong, Eva Lustigova et al. · medRxiv · 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