{
  "abstract": "Background\n\nFibromyalgia is a heterogeneous condition where clinical response to therapies like low-intensity repetitive Transcranial Magnetic Stimulation (Li-rTMS) varies significantly. Existing phenotyping is often limited by cross-sectional designs or lack of external validation. This study aims to develop and validate a reproducible informatics framework to identify stable symptom-based computational phenotypes and evaluate their longitudinal trajectories using multi-centre real-world data (RWD).Methods\n\nWe developed a dual-stage informatics pipeline for data curation and consistency filtering, which was applied to a registry of over 1,600 patients across three independent cohorts (Discovery, Structural Validation, and Longitudinal Validation). Unsupervised learning was performed using a K-means + + architecture on baseline clinical variables (FIQ, WPI, and SSS). Model generalizability was tested using a \"frozen model\" strategy, where centroids and scaling parameters from the discovery cohort were applied to the validation cohorts without re-fitting. Longitudinal stability was assessed by analysing transitions between these clinical symptom phenotypes across the treatment period.Results\n\nThe framework identified three distinct and stable symptom-based computational phenotypes: CP1 (Low Symptom Burden), CP2 (Pain-Dominant), and CP3 (High Multidimensional Burden). The curation pipeline ensured high data quality, maintaining structural consistency across all cohorts. CP1 demonstrated the highest longitudinal stability, acting as a clinical \"sink,\" while CP2 showed the most significant clinical improvement following Li-rTMS intervention. The results confirm that the identified symptom phenotypes are not artifacts of a specific dataset but represent reproducible clinical symptom states.Conclusions\n\nThe proposed informatics framework provides a reliable and transferable methodology for extracting stable clinical symptom phenotypes from heterogeneous real-world registries. By demonstrating cross-cohort validation and longitudinal consistency, this work offers a scalable template for personalized clinical decision-making and stratified management in chronic pain populations, aligning with FAIR data principles.",
  "abstract_shown": true,
  "affiliations": [
    "Departamento de Arquitectura y Tecnología de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Campus Montegancedo, Boadilla del Monte, Madrid, 28660, Spain. nazario.felix@ctb.upm.es.",
    "Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Campus Montegancedo, Carretera M-40, km 38, Pozuelo de Alarcón, Madrid, 28223, Spain. nazario.felix@ctb.upm.es.",
    "Unidad de Fibromialgia y Dolor Crónico. Hospital Universitario San Rafael, Avenida Concha Espina, 32, Madrid, 28016, Spain.",
    "Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Campus Montegancedo, Carretera M-40, km 38, Pozuelo de Alarcón, Madrid, 28223, Spain."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Nazario Felix-Gonzalez"
    },
    {
      "affs": [
        2
      ],
      "name": "Jose-Maria Gomez-Arguelles"
    },
    {
      "affs": [
        3
      ],
      "name": "Ceferino Maestu-Unturbe"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/621ma9ededcae.bib",
  "cited_by": 0,
  "doi": "10.1186/s12911-026-03740-6",
  "fulltext_url": "https://europepmc.org/articles/PMC13726113",
  "id": "621ma9ededcae",
  "issns": [
    "1472-6947"
  ],
  "landing_url": "https://europepmc.org/article/MED/42972570",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2738|Fibromyalgia and Chronic Fatigue Syndrome Research"
    ]
  ],
  "openalex": "W7172500230",
  "pdf_url": "https://europepmc.org/articles/PMC13726113?pdf=render",
  "pmcid": "PMC13726113",
  "pmid": "42972570",
  "published": "2026-08-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/621ma9ededcae.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:20:44Z",
      "native_id": "MED:42972570",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42972570"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2738|Fibromyalgia and Chronic Fatigue Syndrome Research",
      "subfield": "psychiatry"
    }
  ],
  "title": "A reproducible symptom-based computational phenotyping framework for longitudinal fibromyalgia analysis using real-world registry data: a cross-cohort validation study.",
  "title_text": "A reproducible symptom-based computational phenotyping framework for longitudinal fibromyalgia analysis using real-world registry data: a cross-cohort validation study.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-04T15:20:44Z",
  "url": "https://research.draconitic.com/paper/621ma9ededcae",
  "venue": "BMC medical informatics and decision making",
  "versions": []
}