Automated Knowledge Engineering Methods to Improve Consumers' Comprehension of their Health Records

项目总结 今天，比以往任何时候都更多的患者可以在网上访问他们的健康记录。然而，临床缩略语 妨碍患者理解他们的记录，并减少透明度的好处。一种自动化的 临床缩略语扩展系统应具有重大的临床意义和深远的后果 用于改善患者与提供者之间的沟通、共享决策和健康结果。现有系统 扩展临床缩略语的能力有限，主要是因为缺乏全面性(或概括性- 现有缩写词Sense Inventory的可扩充性)。因为发展全面的感觉清单是 困难的、现有的知识工程方法主要集中在开发特定于机构的方法 感觉库存。机构特定的感觉清单可能不能推广到其他地理区域 和医学专科。此外，在每一家美国医疗保健机构开发一份特定机构的感觉清单 组织是不可行的，特别是如果没有目前不存在的自动化方法。 我开发了高级知识工程方法来克服这些限制，通过使用 全自动技术来概括来自不同地理区域和 医学专科。我的方法利用了已经投入到发展机构的广泛资源- 美国的特定感觉库存，并可能有助于将现有的感觉库存推广到没有 开发它们的资源。尽管前景看好，但在优化和评估 这些方法。拟议项目的目标是使用知识工程来改善患者的 了解他们的健康记录，特别是临床缩略语。在目标1中，我将开发新的 知识工程方法，以促进SENSE库存的自动集成，使用文献- 基于质量启发式和暹罗神经网络建立同义词。我将对这些方法进行评估 在两个超过1700万的测试语料库中使用多个指标评估冗余、质量和覆盖率 临床记录。在目标2中，我将评估知识工程方法是否提高了对 60例晚期心力衰竭住院患者的医案记录。有了成功，我将创作小说， 可直接应用于改善患者护理的自动化知识工程方法。这项研究 是为了支持我在哥伦比亚大学生物医学信息学系接受指导的博士培训 (DBMI)在大卫·沃德雷博士、乔治·赫普萨克博士、卡罗尔·弗里德曼博士、苏珊娜·巴肯博士和翁春华博士的领导下， 并将包括深度学习的课程作业，在主要年度会议上的口头报告，以及职业生涯 发展规划，以及其他活动。DBMI通常被认为是最古老和最好的之一 这是世界上同类项目中最好的，并为我发展成为一名 独立、多产的学术研究人员。

PROJECT SUMMARY Today, more patients can access their health records online than ever before. However, clinical acronyms hinder patients' comprehension of their records and decrease the benefits of transparency. An automated system for expanding clinical acronyms should have major clinical significance and far-reaching consequences for improving patient-provider communication, shared decision-making, and health outcomes. Existing systems have limited power to expand clinical acronyms, primarily due to the lack of comprehensiveness (or generali- zability) of existing acronym sense inventories. Because developing comprehensive sense inventories is difficult, existing knowledge engineering methods have primarily focused on developing institution-specific sense inventories. Institution-specific sense inventories may not be generalizable to other geographical regions and medical specialties. Furthermore, developing an institution-specific sense inventory at every US healthcare organization is not feasible, especially without automated methods which currently do not exist. I developed advanced knowledge engineering methods to overcome these limitations through the use of fully automated techniques to generalize existing sense inventories from different geographical regions and medical specialties. My methods leverage the extensive resources already devoted to developing institution- specific sense inventories in the U.S., and may help generalize existing sense inventories to institutions without the resources to develop them. Although promising, challenges remain with the optimization and evaluation of these methods. The objective of the proposed project is to use knowledge engineering to improve patients' comprehension of their health records, focusing specifically on clinical acronyms. In Aim 1, I will develop new knowledge engineering methods to facilitate the automated integration of sense inventories, using literature- based quality heuristics and a Siamese neural network to establish synonymy. I will evaluate these methods using multiple metrics to assess redundancy, quality, and coverage in two test corpora with over 17 million clinical notes. In Aim 2, I will evaluate whether the knowledge engineering methods improve comprehension of doctors' notes in 60 hospitalized patients with advanced heart failure. With success, I will create novel, automated knowledge engineering methods that can be directly applied to improve patient care. This research is in support of my mentored doctoral training at Columbia University Department of Biomedical Informatics (DBMI) under Drs. David Vawdrey, George Hripcsak, Carol Friedman, Suzanne Bakken, and Chunhua Weng, and will include coursework on deep learning, oral presentations at major annual conferences, and career development planning, among other activities. DBMI is frequently recognized as one of the oldest and best programs of its kind in the world, and provides an exception training environment for my development into an independent and productive academic investigator.