{
  "abstract": "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.\n\nMethods: 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.\n\nResults: 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.\n\nConclusions: 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.\n\nKey messages: • AI pipeline converts Google Trends queries into structured, PICO-based guideline questions for public health use.\n\n• LLMs outperform rules in detecting questions, enabling reproducible, scalable insights into population health information needs and surveillance.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "abstract",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "T Makowski"
    },
    {
      "name": "W Neuman"
    },
    {
      "name": "B Sousa-Pinto"
    },
    {
      "name": "M Marques-Cruz"
    },
    {
      "name": "R Vieira"
    },
    {
      "name": "P Jemioło"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eyh9tn0wvhvdy.bib",
  "cited_by": 0,
  "doi": "10.1093/eurpub/ckag169.1236",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13699733/",
  "id": "eyh9tn0wvhvdy",
  "issns": [
    "1101-1262",
    "1464-360X"
  ],
  "landing_url": "https://doi.org/10.1093/eurpub/ckag169.1236",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "6. Poster Displays"
    ],
    [
      "heading",
      "AcademicSubjects/MED00860"
    ],
    [
      "heading",
      "AcademicSubjects/SOC01210"
    ],
    [
      "heading",
      "AcademicSubjects/SOC02610"
    ],
    [
      "heading",
      "DE. Digital health and Artificial Intelligence"
    ],
    [
      "openalex",
      "2713|Data-Driven Disease Surveillance"
    ]
  ],
  "openalex": "W7223664111",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13699733/pdf/?tool=EBI",
  "pmcid": "PMC13699733",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/eyh9tn0wvhvdy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:18Z",
      "native_id": "PMC13699733",
      "source": "pmc",
      "url": "https://doi.org/10.1093/eurpub/ckag169.1236"
    },
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "PMC:PMC13699733",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13699733"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2713|Data-Driven Disease Surveillance",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Pipeline to enable guideline question identification from GoogleTrends with artificial intelligence",
  "title_text": "Pipeline to enable guideline question identification from GoogleTrends with artificial intelligence",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:36:16Z",
  "url": "https://research.draconitic.com/paper/eyh9tn0wvhvdy",
  "venue": "The European Journal of Public Health",
  "versions": []
}