diagnosis prediction 1longitudinal data 1pediatric health records 1risk forecasting 1transformer models 1
From the 1 of 2 linked papers with an AI index.
2 papers
cs.CL2026
Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Miss
Daniel Palacios, Matthew Brady Neeley, Angel Adetomike Otto +6
Secondary use of electronic health records requires de-identification, yet existing systems miss \emph{institutionally situated} protected health information (PHI) such as hospital…
cs.LG2026
TEDDY: A Pediatric Foundation Model for Risk Forewarning from ICD-Coded Diagnostic Histories
Matthew Brady Neeley, Jorge Botas, Johnathan Jia +5
The paper introduces TEDDY, a compact transformer model that learns from pediatric ICD-10 diagnosis histories to predict disease onset and visit timing up to years in advance.