Extracting Complementary and Integrative Health Approaches in Electronic Health Records.

IF 5.9 Q1 Computer Science
Journal of Healthcare Informatics Research Pub Date : 2023-08-17 eCollection Date: 2023-09-01 DOI:10.1007/s41666-023-00137-2
Huixue Zhou, Greg Silverman, Zhongran Niu, Jenzi Silverman, Roni Evans, Robin Austin, Rui Zhang
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引用次数: 0

Abstract

Complementary and Integrative Health (CIH) has gained increasing popularity in the past decades. While the evidence bases to support them are growing, there is still a gap in understanding their effects and potential adverse events using real-world data. The overall goal of this study is to represent information pertinent to both psychological and physical CIH approaches (specifically, using examples of music therapy, chiropractic, and aquatic exercise in this study) in an electronic health record (EHR) system. We also aim to evaluate the ability of existing natural language processing (NLP) systems to identify CIH approaches. A total of 300 notes were randomly selected and manually annotated. Annotations were made for status, symptom, and frequency of each approach. This set of annotations was used as a gold standard to evaluate the performance of NLP systems used in this study (specifically BioMedICUS, MetaMap, and cTAKES) for extracting CIH concepts. Venn diagram was used to investigate the consistency of medical records searching by Current Procedural Terminology (CPT) codes and CIH approaches keywords in SQL. Since CPT codes usually do not have specific mentions of CIH approaches, the Venn diagram had less overlap with those found in clinical notes for all three CIH therapies. The three NLP systems achieved 0.41 in average lenient match F1-score in all three CIH approaches, respectively. BioMedICUS achieved the best performance in aquatic exercise with an F1-score of 0.66. This study contributes to the overall representation of CIH in clinical note and lays a foundation for using EHR for clinical research for CIH approaches.

从电子健康记录中提取互补和综合的健康方法。
在过去的几十年里,补充和综合健康(CIH)越来越受欢迎。尽管支持它们的证据基础正在增加,但使用真实世界的数据来理解它们的影响和潜在的不良事件仍然存在差距。本研究的总体目标是在电子健康记录(EHR)系统中表示与心理和物理CIH方法相关的信息(特别是,在本研究中使用音乐治疗、脊椎按摩和水上运动的例子)。我们还旨在评估现有自然语言处理(NLP)系统识别CIH方法的能力。共有300个音符被随机选择并手动注释。对每种方法的状态、症状和频率进行了注释。这组注释被用作评估本研究中用于提取CIH概念的NLP系统(特别是BioMedICUS、MetaMap和cTAKES)性能的金标准。Venn图用于研究SQL中当前程序术语(CPT)代码和CIH方法关键字搜索病历的一致性。由于CPT代码通常没有具体提及CIH方法,Venn图与所有三种CIH疗法的临床记录中发现的重叠较少。三种NLP系统在所有三种CIH方法中的平均宽松比赛F1得分分别达到0.41。BioMedICUS在水上运动中取得了最好的成绩,F1得分为0.66。本研究有助于CIH在临床笔记中的总体表现,并为使用EHR进行CIH方法的临床研究奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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