Author

Animesh Agarwal

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#machine learning Preprint Aug 2026

A Multi-Agent Pipeline for Source-Grounded Synthetic Note Generation from Longitudinal Structured EHR

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

#artificial intelligence Preprint Sep 2026

LearnActCoder: Role-Aware Error Memory for Adaptive Clinical Coding Agents

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

Review Aug 2026

Structured Evidence Routing for Incident Risk Prediction from Multimodal Longitudinal EHRs

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