Medical Code Classification via Linear Probing of LLM Activations
Tags: Healthcare AI · Interpretability
This project investigates multi-label medical code classification by training linear probes on Large Language Model (LLM) activations. We extract layer-wise attention head activations from medical-domain LLMs and use Ridge regression classifiers to predict relevant medical disciplines from clinical descriptions. The approach enables interpretable analysis of which model components are most informative for medical domain classification tasks.