{
  "abstract": "Introduction: Thrombocytopenia has heterogeneous mechanisms, including peripheral destruction, consumption, and reduced marrow production. This study developed an unsupervised global complete blood count/research‐use‐only/cell population data (CBC/RUO/CPD) phenotyping model and evaluated post hoc whether thrombocytopenia severity and clinically adjudicated mechanisms were enriched within the resulting clusters.\n\nMethods: Retrospective haematology analyser data from 254 289 unique specimens were analysed after cleaning 537 279 extracted rows. Exploratory missingness filtering retained 123 features and 173 851 complete‐case specimens. The final model used a refined 41‐feature CBC_EXTENDED complete‐case dataset containing 241 591 specimens. Features were standardised, reduced using principal component analysis to 20 components explaining 92.94% of cumulative variance, and clustered using k‐means. Thrombocytopenia thresholds and electronic health record (EHR) adjudicated primary mechanism labels were evaluated post hoc.\n\nResults: The final global k‐means model (k = 4) generated four broad haematological clusters comprising 14%–37% of specimens. Internal separation was modest but interpretable, with sampled silhouette 0.10, Davies–Bouldin 2.21, and Calinski–Harabasz 6536.92 in the fixed 60 000 specimen model comparison analysis. Cluster 3 showed progressive post hoc enrichment with thrombocytopenia severity, accounting for approximately 46%, 60%, and 71% of specimens with platelet counts < 150, < 100, and < 50 × 109/L, respectively. In 225 clinically annotated thrombocytopenic specimens, k‐means k = 4 showed limited but non‐random concordance with adjudicated mechanisms (purity 0.59; adjusted Rand index 0.14; normalised mutual information 0.18).\n\nConclusion: Unsupervised global CBC/RUO/CPD clustering identified broad laboratory phenotypes with post hoc enrichment for thrombocytopenia severity and adjudicated mechanisms. These findings support underutilised analyser‐derived parameters for laboratory phenotyping and hypothesis generation, not standalone diagnosis.",
  "abstract_shown": true,
  "affiliations": [
    "School of Dentistry and Medical Sciences, Faculty of Science and Health Charles Sturt University Wagga Wagga New South Wales Australia",
    "Haematology Laboratory Liverpool Hospital Liverpool New South Wales Australia",
    "Sydney Centres for Thrombosis and Haemostasis, Institute of Clinical Pathology and Medical Research (ICPMR) and Research and Education Network (REN) Westmead Hospital Westmead New South Wales Australia",
    "AI and Cyber Futures Centre Charles Sturt University Bathurst New South Wales Australia",
    "School of Computing, Mathematics and Engineering Charles Sturt University Bathurst New South Wales Australia",
    "Department of Haematology, Liverpool Hospital, Faculty of Medicine & Health University of New South Kensington New South Wales Australia"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Mohammad A. Altememi",
      "orcid": "0009-0004-2927-6868"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Emmanuel J. Favaloro",
      "orcid": "0000-0002-2103-1661"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Md Zahidul Islam",
      "orcid": "0000-0002-4868-4945"
    },
    {
      "affs": [
        5
      ],
      "name": "Sara Ng",
      "orcid": "0000-0002-5620-1659"
    },
    {
      "affs": [
        5
      ],
      "name": "Danny Hsu",
      "orcid": "0000-0002-9577-5401"
    },
    {
      "affs": [
        0
      ],
      "name": "Abishek B. Santhakumar",
      "orcid": "0000-0003-1836-5035"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7a8ekd4e9rgh9.bib",
  "cited_by": 0,
  "doi": "10.1111/ijlh.70217",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13729941/",
  "id": "7a8ekd4e9rgh9",
  "issns": [
    "1751-5521",
    "1751-553X"
  ],
  "landing_url": "https://doi.org/10.1111/ijlh.70217",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Cell Counting/Automation"
    ],
    [
      "heading",
      "ORIGINAL ARTICLE"
    ],
    [
      "heading",
      "Original Article"
    ],
    [
      "openalex",
      "2720|Platelet Disorders and Treatments"
    ]
  ],
  "openalex": "W7202175925",
  "pdf_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/ijlh.70217",
  "pmcid": "PMC13729941",
  "pmid": "42578661",
  "published": "2026-08-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7a8ekd4e9rgh9.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:22:39Z",
      "native_id": "PMC13729941",
      "source": "pmc",
      "url": "https://doi.org/10.1111/ijlh.70217"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2720|Platelet Disorders and Treatments",
      "subfield": "general"
    }
  ],
  "title": "Unsupervised Global CBC/RUO/CPD Phenotyping Identifies Haematological Clusters Enriched for Thrombocytopenia Severity and Mechanisms",
  "title_text": "Unsupervised Global CBC/RUO/CPD Phenotyping Identifies Haematological Clusters Enriched for Thrombocytopenia Severity and Mechanisms",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:22:39Z",
  "url": "https://research.draconitic.com/paper/7a8ekd4e9rgh9",
  "venue": "International Journal of Laboratory Hematology",
  "versions": []
}