系统梳理2014-2025年心血管病NLP研究，揭示从规则系统到大模型的演进趋势。
Natural Language Processing for Cardiology: A Narrative Review
- 综述265篇文献，分析NLP在心脏病领域的应用范式与任务类型。
- 发现方法演进：由规则系统转向大语言模型，覆盖多种疾病与数据源。
- 适合医疗AI研究者参考，尤其关注可解释性与多模态融合方向。
心血管疾病在现代社会中日益普遍，对全球健康造成深远影响。这些疾病具有复杂性和多因素特征，受遗传倾向、生活方式及社会经济与临床因素共同影响。相关资讯分散于患者叙述、病历记录和科学文献等文本数据中。自然语言处理（NLP）已成为分析此类非结构化数据的强大工具，使医疗专业人员与研究人员能够获得更深入的洞察，可能推动心脏病的诊断、治疗与预防变革。本综述系统回顾了2014至2025年间心血管病领域的NLP研究，通过六大数据库检索并经严格筛选，共识别出265篇相关文章。每项研究均从NLP范式、任务类型、疾病类别与数据来源等多个维度进行分析。结果表明，该领域在上述维度上具有显著多样性，反映其广度与演进历程。时间序列分析进一步揭示方法趋势：从基于规则的系统逐步发展为大语言模型（LLMs）。最后，我们讨论关键挑战与未来方向，如开发可解释的大型语言模型及整合多模态数据。据我们所知，这是迄今关于心血管病NLP研究最全面的综合成果。
原文摘要 · Abstract (English)
Cardiovascular diseases are becoming increasingly prevalent in modern society, with a profound impact on global health and well-being. These Cardiovascular disorders are complex and multifactorial, influenced by genetic predispositions, lifestyle choices, and diverse socioeconomic and clinical factors. Information about these interrelated factors is dispersed across multiple types of textual data, including patient narratives, medical records, and scientific literature. Natural language processing (NLP) has emerged as a powerful approach for analysing such unstructured data, enabling healthcare professionals and researchers to gain deeper insights that may transform the diagnosis, treatment, and prevention of cardiac disorders. This review provides a comprehensive overview of NLP research in cardiology from 2014 to 2025. We systematically searched six literature databases for studies describing NLP applications across a range of cardiovascular diseases. After a rigorous screening process, we identified 265 relevant articles. Each study was analysed across multiple dimensions, including NLP paradigms, cardiology-related tasks, disease types, and data sources. Our findings reveal substantial diversity within these dimensions, reflecting the breadth and evolution of NLP research in cardiology. A temporal analysis further highlights methodological trends, showing a progression from rule-based systems to large language models. Finally, we discuss key challenges and future directions, such as developing interpretable LLMs and integrating multimodal data. To the best of our knowledge, this review represents the most comprehensive synthesis of NLP research in cardiology to date.
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