A generative transformer trained on tokenized patient histories can be repurposed as a dynamic early-warning system, outperforming standard clinical scores and tabular baselines on MIMIC-IV emergency department predictions.
Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis
1 Pith paper cite this work, alongside 311 external citations. Polarity classification is still indexing.
1
Pith paper citing it
311
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Foundation Model of Electronic Medical Records for Adaptive Risk Estimation
A generative transformer trained on tokenized patient histories can be repurposed as a dynamic early-warning system, outperforming standard clinical scores and tabular baselines on MIMIC-IV emergency department predictions.