{
  "abstract": "BACKGROUND AND AIMS: Inflammatory bowel disease (IBD) case reports provide rich longitudinal insights but have rarely been analyzed using quantitative text-mining approaches. This study applied unsupervised machine learning to PubMed-indexed IBD case reports to identify long-term thematic structures spanning 60 years and evaluate whether major historical milestones in IBD care can be reconstructed from biomedical texts. METHODS: Case reports indexed under the keyword \"inflammatory bowel disease\" were retrieved from PubMed (1960-2025). Titles, key words, and abstracts were concatenated and preprocessed before TF-IDF vectorization. Non-negative matrix factorization (NMF) was applied to extract latent topics, followed by KMeans clustering using the optimal topic number selected by silhouette evaluation (2-15 topics). Cluster characteristics were summarized using report counts and term frequency-inverse document frequency (TF-IDF) statistics. Top discriminative key words were used to assign data-driven topic labels. All analyses were performed in Python 3.10.5 (PyCharm 2022.1.3) using pandas, numpy, scikit-learn, matplotlib, and seaborn. RESULTS: A total of 18,458 case reports were analyzed. Across all time periods, two highly stable clusters consistently emerged, corresponding to Crohn's disease and ulcerative colitis. Early decades (1960-1989) emphasized pathology and complication-focused descriptions. Reports from the 1990s showed increasing terminology related to diagnosis and emerging therapies. From 2000 onward, infliximab-related and treatment focused terms predominated, paralleling the rise of biology. After 2010, clusters reflected diversified therapeutic strategies, including attention to extraintestinal manifestations and biologic or small-molecule therapies. CONCLUSIONS: Unsupervised machine learning successfully reconstructed important historical changes in IBD management, demonstrating that a large case report text corpus captures the evolution of clinical concepts and treatment paradigms over 60 years.",
  "abstract_shown": true,
  "affiliations": [
    "Editorial Department, The Japanese Society of Internal Medicine, Tokyo, Japan. n-ogasawara@naika.or.jp"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Naruaki Ogasawara",
      "orcid": "0000-0003-0437-2824"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/esmbvtznkb9vc.bib",
  "cited_by": 11,
  "doi": "10.15403/jgld-6740",
  "fulltext_url": null,
  "id": "esmbvtznkb9vc",
  "issns": [
    "1221-4167",
    "1841-8724",
    "1842-1121"
  ],
  "landing_url": "https://doi.org/10.15403/jgld-6740",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1311|Inflammatory Bowel Disease"
    ],
    [
      "openalex",
      "2715|Inflammatory Bowel Disease"
    ]
  ],
  "openalex": "W7166100834",
  "pdf_url": "https://jgld.ro/jgld/index.php/jgld/article/download/6740/2331",
  "pmcid": null,
  "pmid": "42365648",
  "published": "2026-06-26",
  "publisher": "Editura Medicală Universitară Iuliu Hatieganu",
  "ris": "https://research.draconitic.com/paper/esmbvtznkb9vc.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T13:21:07Z",
      "native_id": "W7166100834",
      "source": "openalex",
      "url": "https://doi.org/10.15403/jgld-6740"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1311|Inflammatory Bowel Disease",
      "subfield": "genetics-genomics"
    },
    {
      "field": "medicine",
      "origin": "openalex:2715|Inflammatory Bowel Disease",
      "subfield": "general"
    }
  ],
  "title": "Unsupervised Machine Learning of Inflammatory Bowel Disease Case Reports Reveals Decades of Evolving Research Themes",
  "title_text": "Unsupervised Machine Learning of Inflammatory Bowel Disease Case Reports Reveals Decades of Evolving Research Themes",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:51Z",
  "url": "https://research.draconitic.com/paper/esmbvtznkb9vc",
  "venue": "Journal of Gastrointestinal and Liver Diseases",
  "versions": []
}