Automated Multitier Tagging of Chinese Online Health Education Resources Using a Large Language Model: Development and Validation Study.

ABSTRACT:

Background

Precision health promotion, which aims to tailor health messages to individual needs, is hampered by the lack of structured metadata in vast digital health resource libraries. This bottleneck prevents scalable, personalized content delivery and exacerbates information overload for the public.

Objective

This study aimed to develop, deploy, and validate an automated tagging system using a large language model (LLM) to create the foundational metadata infrastructure required for tailored health communication at scale.

Methods

We developed a comprehensive, 3-tier health promotion taxonomy (10 primary, 34 secondary, and 90,562 tertiary tags) using a hybrid Delphi and corpus-mining methodology. We then constructed a hybrid inference pipeline by fine-tuning a Baic

SUBMITTER: Meng J 

PROVIDER: S-EPMC12756663 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature