BACKGROUND Previous research has developed methods to construct acronym sense inventories from a single institutional corpus. Although beneficial, a sense inventory constructed from a single institutional corpus is not generalizable, because acronyms from different geographic regions and medical specialties vary greatly. OBJECTIVE Develop an automated method to harmonize sense inventories from different regions and specialties towards the development of a comprehensive inventory. METHODS The method involves integrating multiple source sense inventories into one centralized inventory and cross-mapping redundant entries to establish synonymy. To evaluate our method, we integrated 8 well-known source inventories into one comprehensive inventory (or metathesaurus). For both the metathesaurus and its sources, we evaluated the coverage of acronyms and their senses on a corpus of 1 million clinical notes. The corpus came from a different institution, region, and specialty than the source inventories. RESULTS In the evaluation using clinical notes, the metathesaurus demonstrated an acronym (short form) micro-coverage of 94.3%, representing a substantial increase over the two next largest source inventories, the UMLS LRABR (74.8%) and ADAM (68.0%). The metathesaurus demonstrated a sense (long form) micro-coverage of 99.6%, again a substantial increase compared to the UMLS LRABR (82.5%) and ADAM (55.4%). CONCLUSIONS Given the high coverage, harmonizing acronym sense inventories is a promising methodology to improve their comprehensiveness. Our method is automated, leverages the extensive resources already devoted to developing institution-specific inventories in the United States, and may help generalize sense inventories to institutions who lack the resources to develop them. Future work should address quality issues in source inventories and explore additional approaches to establishing synonymy.

中文翻译：

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一种统一临床缩写和首字母缩写含义清单的方法。

背景技术先前的研究已经开发了从单个机构语料库构建首字母缩写词意义清单的方法。尽管有益，但是不能归纳出由单个机构语料库构建的有意识的清单，因为来自不同地理区域和医学专业的首字母缩写存在很大差异。目的开发一种自动化方法，以协调来自不同地区和专业的感性库存，以建立综合库存。方法该方法涉及将多个源感知清单集成到一个集中式清单中，并交叉映射冗余条目以建立同义词。为了评估我们的方法，我们将8个著名的来源清单集成到一个综合清单（或元同义词库）中。对于元同义词库及其来源，我们在一百万个临床笔记的语料库中评估了首字母缩写词及其含义的覆盖范围。语料库与原始资源清单不同，来自不同的机构，地区和专业。结果在使用临床记录进行的评估中，同义词库显示了94.3％的首字母缩略词（简写形式）微覆盖率，较接下来的两个最大来源库存（UMLS LRABR（74.8％）和ADAM（68.0％））显着增加。元同义词库显示有感（长形式）微覆盖率为99.6％，与UMLS LRABR（82.5％）和ADAM（55.4％）相比，又有了实质性的增长。结论鉴于覆盖率很高，协调首字母缩写词意识清单是提高其综合性的一种有前途的方法。我们的方法是自动化的 利用已经投入的大量资源来开发美国特定于机构的清单，并且可以帮助将有意识的清单推广到缺乏资源来开发它们的机构。未来的工作应该解决源清单中的质量问题，并探索建立同义词的其他方法。