Next-encounter ICD forecasting predicts which standardized diagnosis codes will be documented at a future visit from the longitudinal record available beforehand. The task is prospective and multi-label: the target note does not yet exist, and several codes may be correct. Structured EHR foundation models capture recur...
Junda Wang, Meysam Ghaffari, Akshat Choube et al.· 0 citations
Structured EHR is abundant but sparse, coded, and difficult to use directly for note-centric clinical modeling. We present MedNotes, a multi-agent synthetic data generation pipeline that converts longitudinal structured EHR into source-grounded clinical note representations under explicit quality control. MedNotes trea...
Nina Fatehi, Reihaneh Hassanzadeh, Meysam Ghaffari et al.· 0 citations
Large language models can process increasingly long prompts, yet their ability to locate and use decisive evidence may degrade as irrelevant or confusable context is added. We formulate this phenomenon, which we call context poisoning, as extreme-value interference in attention: the decisive-evidence score is upper-bou...
Meysam Ghaffari, Nina Fatehi, Bhaskar Sen et al.· 0 citations
The results are consistent with structured, feedback-derived error memory being useful for adapting clinical coding behavior across cases without weight updates or changes to the underlying workflow.
Meysam Ghaffari, Bhaskar Sen, Nasim Sabetpour et al.· 0 citations
This work proposes structured evidence routing, a router-predictor-reviewer workflow that separates full-record access from disease-specific assessment, and suggests that routing, laboratory evidence, task guidance, and review each contribute to performance.
Animesh Agarwal, Meysam Ghaffari, Nina Fatehi et al.· 0 citations
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