Few-Shot Large Language Models for Actionable Triage Categorization of Online Patient Inquiries
Online patient inquiries are often informal, incomplete, and written before professional assessment, yet they must still be routed to an appropriate level of clinical follow-up. We study this as a four-class actionable triage task -- self-care, schedule-visit, urgent-clinician-review, or emergency-referral, and ask whether prompted large language models (LLMs) can support such routing under low-resource labeling conditions. Using the public HealthCareMagic-100K corpus, we construct a 300-example human calibrated gold evaluation set, a 700-example auto-labeled silver training set, and a 40-example few-shot pool. We compare Term Frequency-Inverse Document Frequency (TF-IDF) and Bidirectiona...
| Authors | Liqi Zhou, Jiafu Li |
|---|---|
| Primary category | cs.CL |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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