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PreprintAll rights reservedmedRxiv

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

  • W. Chen1
  • B. Zhou
  • R. S. Zeiger
  • W. W. Crawford
  • M. Schatz
  • E. J. Puttock
  • S. Xu
  • M. T. Slaughter
  • F. Xie
  1. 1Kaiser Permanente Southern California - Department of Research and Evaluation
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Cite

BibTeXRIS
BibTeX
@misc{chen2026rethinking,
  title = {{Rethinking Input Complexity in Transformer-Based Clinical Prediction: Implications for Feature Dimensionality and Sequence Length in Longitudinal EHR Data}},
  author = {W. Chen and B. Zhou and R. S. Zeiger and W. W. Crawford and M. Schatz and E. J. Puttock and S. Xu and M. T. Slaughter and F. Xie},
  howpublished = {medRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.03.26362165},
  url = {https://www.medrxiv.org/content/10.64898/2026.09.03.26362165v1},
}
RIS
TY  - UNPB
TI  - Rethinking Input Complexity in Transformer-Based Clinical Prediction: Implications for Feature Dimensionality and Sequence Length in Longitudinal EHR Data
AU  - W. Chen
AU  - B. Zhou
AU  - R. S. Zeiger
AU  - W. W. Crawford
AU  - M. Schatz
AU  - E. J. Puttock
AU  - S. Xu
AU  - M. T. Slaughter
AU  - F. Xie
PY  - 2026
DA  - 2026/09/03
PB  - medRxiv
DO  - 10.64898/2026.09.03.26362165
UR  - https://www.medrxiv.org/content/10.64898/2026.09.03.26362165v1
ER  -