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

自动化知识工程方法可提高消费者对其健康记录的理解

• 批准号: 9895430
• 负责人: Lisa Grossman Liu
• 金额: $ 4.74万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2021-01-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9895430
• 关键词: Abbreviations; Award; Clinical; Clinical Medicine; Comprehension; Controlled Vocabulary; Development; Development Plans; Engineering; Environment; Equipment and supply inventories; Evaluation; Future; Geographic Locations; Goals; Grant; Health; Healthcare; Heart failure; Hospitals; Informatics; Information Resources; Institution; Knowledge; Literature; Machine Learning; Measures; Medical; Medical Records; Mentors; Mentorship; Methods; Natural Language Processing; Oral; Outcome; Patient Care; Patients; Performance; Physicians; Positioning Attribute; Publishing; Questionnaires; Records; Research; Research Methodology; Research Personnel; Resources; Safety; Source; Support System; System; Techniques; Technology; Testing; Training; Unified Medical Language System; Universities; acronyms; base; biomedical informatics; career development; clinically significant; deep learning; doctoral student; federal policy; health care service organization; health record; heuristics; improved; information organization; medical specialties; method development; multidisciplinary; neural network; novel; patient portal; patient-clinician communication; programs; shared decision making; success; symposium; tool

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.

项目摘要 如今，越来越多的患者可以在线访问他们的健康记录。然而，临床缩略语 阻碍患者理解其记录并降低透明度的好处。自动 扩展临床缩略语的系统应该具有重大的临床意义和深远的影响 用于改善患者与提供者的沟通、共同决策和健康结果。现有系统 扩展临床缩略语的能力有限，主要是由于缺乏全面性（或通用性）， 可用性）。因为开发全面的感官清单 困难的，现有的知识工程方法主要集中在开发机构特定的 感官清单特定机构的感觉清单可能无法推广到其他地理区域 和医学专业。此外，在每个美国医疗保健机构开发一个机构特定的感觉清单， 组织是不可行的，特别是没有目前不存在的自动化方法。 我开发了先进的知识工程方法，通过使用 全自动化技术，从不同的地理区域归纳现有的感官清单， 医学专业。我的方法充分利用了已经投入到发展机构中的大量资源- 在美国的特定感官清单，并可能有助于将现有的感官清单推广到没有 资源来开发它们。尽管前景看好，但在优化和评估 这些方法。该项目的目标是利用知识工程来提高患者的 理解他们的健康记录，特别关注临床缩略语。在目标1中，我将开发新的 知识工程方法，以促进自动集成的感觉库存，使用文献- 基于质量分类法和暹罗神经网络建立同义词。我将评估这些方法 使用多个指标来评估两个测试语料库中超过1700万个的冗余、质量和覆盖率 临床笔记在目标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生记录。有了成功，我将创造新的， 自动化知识工程方法，可以直接应用于改善病人护理。本研究 是为了支持我在哥伦比亚大学生物医学信息学系的博士培训 （DBMI），由大卫瓦胡里、乔治赫里普萨克、卡罗尔弗里德曼、苏珊娜巴肯和翁春华博士领导， 并将包括深度学习课程，在主要年度会议上的口头演讲，以及职业生涯 发展规划等活动。DBMI经常被认为是最古老和最好的 它的同类程序在世界上，并提供了一个例外的培训环境，我的发展成为一个 独立和富有成效的学术研究者。

项目成果

A deep database of medical abbreviations and acronyms for natural language processing.

自然语言处理的医学缩写和首字母缩写的深度数据库。

• DOI: 10.1038/s41597-021-00929-4
• 发表时间: 2021-06-02
• 期刊: Scientific data
• 影响因子: 9.8
• 作者: Grossman Liu L;Grossman RH;Mitchell EG;Weng C;Natarajan K;Hripcsak G;Vawdrey DK
• 通讯作者: Vawdrey DK