构建疾病命名分类体系，揭示命名背后的医学文化规律。
Nomenclature Ontology for Medical And Disease names (NOMAD): taxonomy of types and origins of disease names
- 提出NOMAD元分类框架，按命名来源与类型对疾病名分级归类。
- 覆盖2.25万条ICD-10-CM词条，99.1%准确分类，平均每项2.12个标签。
- 发现命名受学科传统影响大，女性命名者仅占2.6%，地理命名占比偏低。
人类疾病的命名历经数百年发展，融合希腊、拉丁及阿拉伯术语，反映不同时代的医学认知特征。尽管命名方式差异显著，却缺乏系统性分类框架。本文提出疾病命名本体（NOMAD），构建包含9个主类、20个子类的两级分类体系，并通过三阶段机器学习管道对ICD-10-CM 2026年字母索引中的22,548条条目进行多标签分类。分类准确率达99.1%，平均每项2.12个标签。解构结果显示：解剖学类占比最高（63.8%），其次为描述性（48.4%）和病理生理类（40.2%）；以人名命名的疾病中仅2.6%来自女性；地理命名仅占1.9%。各章节命名模式差异显著：传染病章节以病因为主，地理标签比例高；循环系统以解剖与病理生理类为主；精神与行为障碍则以社会行为类最为普遍。人工抽样验证显示整体一致性达70%，部分一致26%（宏观加权科恩卡帕系数0.832）。
原文摘要 · Abstract (English)
The nomenclature of human disease has developed organically over the past centuries using Greek, Latin, and Arabic terminology and reflects the idiosyncrasies of different eras of medical discovery. Despite evident heterogeneity in naming practices, no systematic framework exists for characterising these conventions across all diseases. In this paper, we describe the Nomenclature Ontology for Medical And Disease names (NOMAD), a meta-taxonomy that classifies disease names according to their naming conventions. We developed a two-level taxonomy comprising 9 top-level categories and 20 subcategories and applied it to 22,548 index entries from the ICD-10-CM 2026 Alphabetical Index in a scalable three-stage machine learning-driven classification pipeline. Classification was multi-label, reflecting the compositional nature of medical nomenclature. We classified 99.1% of terms with a mean of 2.12 labels per entry. Anatomical categories were the most prevalent (63.8% of entries), followed by Descriptive (48.4%) and Pathophysiological (40.2%), while Eponymous and Geographical labels were less common than their cultural prominence might suggest (9.7% and 1.9% respectively). Among all Eponymous diseases, we identified only 57 (2.6%) of diseases named after a female person. We manually reviewed a random sample of n=2,255 entries (10%) for accuracy and calculated a full agreement rate of 70% and partial agreement rate of 26% (macro-averaged Cohen's Kappa score 0.832). Naming convention profiles varied substantially across ICD-10-CM chapters, reflecting specialty-specific epistemological traditions: infectious disease chapters were dominated by etiological labels and showed the highest proportion of geographical region related labels, the circulatory chapter by anatomical and pathophysiological labels, and mental and behavioural disorders showed the highest prevalence of socio-behavioral labels.
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