电子健康记录笔记中医学术语的适应性远程监督排序
Ranking medical jargon in electronic health record notes by adapted distant supervision
摘要：
目的：允许患者通过在线患者门户网站访问自己的电子健康记录（EHR）笔记有可能改善以患者为中心的护理。然而，医学术语，其中大量的电子病历笔记，已被证明是一个障碍，病人的电子病历的理解。现有的知识库，链接医疗行话奠定术语或定义在缓解这一问题发挥了重要作用，但在电子健康档案中的医疗行话覆盖率低。我们开发了一种数据驱动的方法，挖掘电子病历，以确定和排名的基础上，其重要性的患者的医疗行话，以支持建设以电子病历为中心的外行语言资源。方法：我们开发了一个创新的适应性远程监督（ADS）模型的基础上支持向量机排名医疗行话从电子病历。对于远程监督，我们利用了开放获取的协作消费者健康词汇表，这是一个将非专业术语与医学术语联系起来的大型公共资源。我们探索了统一医学语言系统的知识为基础的功能和分布式的单词表示从未标记的大型语料库学习。我们使用医生确定的重要医学术语评估ADS模型。结果如下：我们的ADS模型显著超过了两种最先进的自动术语识别方法TF*IDF和C-Value，分别产生0.810与0.710和0.667的ROC-AUC。我们的模型在对从7,500多个EHR叙述中挖掘的10万多个候选术语进行排名后，确定了1万个重要的医学术语。结论：我们的工作是丰富词汇资源的重要一步，将医学术语与术语/定义联系起来，以支持患者对EHR的理解。已确定的医学术语及其排名可应要求提供。
英文摘要：
Objective: Allowing patients to access their own electronic health record (EHR) notes through online patient portals has the potential to improve patient-centered care. However, medical jargon, which abounds in EHR notes, has been shown to be a barrier for patient EHR comprehension. Existing knowledge bases that link medical jargon to lay terms or definitions play an important role in alleviating this problem but have low coverage of medical jargon in EHRs. We developed a data-driven approach that mines EHRs to identify and rank medical jargon based on its importance to patients, to support the building of EHR-centric lay language resources. Methods: We developed an innovative adapted distant supervision (ADS) model based on support vector machines to rank medical jargon from EHRs. For distant supervision, we utilized the open-access, collaborative consumer health vocabulary, a large, publicly available resource that links lay terms to medical jargon. We explored both knowledge-based features from the Unified Medical Language System and distributed word representations learned from unlabeled large corpora. We evaluated the ADS model using physician-identified important medical terms. Results: Our ADS model significantly surpassed two state-of-the-art automatic term recognition methods, TF*IDF and C-Value, yielding 0.810 ROC-AUC versus 0.710 and 0.667, respectively. Our model identified 10K important medical jargon terms after ranking over 100K candidate terms mined from over 7,500 EHR narratives. Conclusion: Our work is an important step towards enriching lexical resources that link medical jargon to lay terms/definitions to support patient EHR comprehension. The identified medical jargon terms and their rankings are available upon request.
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