Background: More than 400,000 biomedical concepts and some of their relationships are contained in SnomedCT, a comprehensive biomedical ontology. However, their concept names are not always readily interpretable by non-experts, or patients looking at their own electronic health records (EHR). Clear definitions or descriptions in understandable language are often not available. Therefore, generating human-readable definitions for biomedical concepts might help make the information they encode more accessible and understandable to a wider public. Objective: In this article, we introduce the Automatic Glossary of Clinical Terminology (AGCT), a large-scale biomedical dictionary of clinical concepts generated using high-quality information extracted from the biomedical knowledge contained in SnomedCT. Methods: We generate a novel definition for every SnomedCT concept, after prompting the OpenAI Turbo model, a variant of GPT 3.5, using a high-quality verbalization of the SnomedCT relationships of the to-be-defined concept. A significant subset of the generated definitions was subsequently judged by NLP researchers with biomedical expertise on 5-point scales along the following three axes: factuality, insight, and fluency. Results: AGCT contains 422,070 computer-generated definitions for SnomedCT concepts, covering various domains such as diseases, procedures, drugs, and anatomy. The average length of the definitions is 49 words. The definitions were assigned average scores of over 4.5 out of 5 on all three axes, indicating a majority of factual, insightful, and fluent definitions. Conclusion: AGCT is a novel and valuable resource for biomedical tasks that require human-readable definitions for SnomedCT concepts. It can also serve as a base for developing robust biomedical retrieval models or other applications that leverage natural language understanding of biomedical knowledge.

翻译：背景：SnomedCT 作为综合性生物医学本体，包含超过40万个生物医学概念及其部分关系。然而，其概念名称对非专业人员或查看电子健康档案的患者而言未必易于理解。清晰的定义或通俗易懂的描述往往缺失。因此，为生物医学概念生成人类可读的定义，有助于让更广泛的受众理解并利用这些概念所编码的信息。目的：本文提出自动生成临床术语词汇表——基于SnomedCT中提取的高质量生物医学知识构建的大规模临床概念词典。方法：通过利用待定义概念的SnomedCT关系的高质量语言化表述，向OpenAI Turbo模型（GPT 3.5的变体）发送提示，为每个SnomedCT概念生成全新定义。随后，由具备生物医学专业知识的研究人员从事实性、洞察力和流畅性三个维度，对生成定义中的重要子集进行5分制评估。结果：AGCT包含422,070个SnomedCT概念的计算机生成定义，涵盖疾病、手术、药物和解剖学等多个领域。定义平均长度为49个单词。所有三个维度的平均得分均超过4.5分（满分5分），表明绝大多数定义具有事实性、洞察力和流畅性。结论：AGCT是生物医学任务中需要人类可读SnomedCT概念定义的全新宝贵资源，亦可作为开发鲁棒生物医学检索模型或其他利用生物医学知识自然语言理解应用的基础。