Recursive learning architecture improves zero-shot clinical coding F1 from 0.318 to 0.605
JMIR preprint: recursive memory raises zero-shot clinical coding F1 from 0.318 to 0.605 after 20 iterations.
Recursive learning architecture improves zero-shot clinical coding F1 from 0.318 to 0.605
Original title: A Recursive Learning Architecture for Zero-Shot Automated Clinical Coding, a methodological study
Authors: Natalia Castaño-Villegas, Raúl Escandón, Katherine Monsalve, Jose Zea, Laura Velásquez
Venue: JMIR Preprints — Preprint #98279
Status: Preprint, under journal review
Headline metrics: F1 0.318 baseline → 0.527 with recursive memory → 0.605 after 20 iterations
This methodological study evaluates a recursive learning architecture for automated clinical coding without task-specific examples. The key result is a stepwise gain from a 0.318 F1 baseline to 0.527 with recursive memory, then to 0.605 after 20 iterations.
The work is currently a preprint under journal review, so the evidence card presents it separately from peer-reviewed publications while still linking users to the source manuscript.