Pipeline to enable guideline question identification from GoogleTrends with artificial intelligence

Objectives

For specific topics or keywords, Google Trends lists the most common related search queries performed by users. Although these queries may reflect public information needs from a public health perspective, they are rarely directly usable for clinical guideline development. We aimed to develop a reproducible pipeline that transforms Google Trends queries into structured guideline questions and other outputs for guideline development and public health surveillance.

Methods

The pipeline has five stages: (1) collection of Google Trends queries and metadata for keywords, location, and time period; (2) cleaning, normalization, deduplication, and noise removal; (3) identification of questions using rule-based keywords or large language models (LLMs); (4) classification into symptoms/diagnosis, treatment, safety, prevention, and prognosis; (5) extraction of PICO elements and generation of guideline questions. The system supports multiple LLM providers, including cloud and local Ollama inference, with typed outputs and CSV export for reproducibility.

Results

The pipeline produced two outputs: a classified question dataset and a PICO dataset. It enabled comparison between rule-based and LLM-based identification, with higher recall for LLMs. The system consistently transformed unstructured search queries into structured guideline questions relevant for evidence synthesis and public health monitoring.

Conclusions

This pipeline translates population search behavior into structured guideline questions. From a public health perspective, it enables insight into information needs and may support surveillance of emerging concerns. PICO extraction helps identify relevant populations and outcomes in a patient-centered way. The modular design ensures transparency and reproducibility and can be extended to social media and other infodemiology sources for public health intelligence applications. It supports timely evidence-informed decision making in public health.

Key messages

• AI pipeline converts Google Trends queries into structured, PICO-based guideline questions for public health use.

• LLMs outperform rules in detecting questions, enabling reproducible, scalable insights into population health information needs and surveillance.