Towards comprehensive longitudinal healthcare data capture

Delroy Cameron, Varun Bhagwan, A. Sheth
{"title":"Towards comprehensive longitudinal healthcare data capture","authors":"Delroy Cameron, Varun Bhagwan, A. Sheth","doi":"10.1109/BIBMW.2012.6470310","DOIUrl":null,"url":null,"abstract":"The ability to connect the dots in structured background knowledge and also across scientific literature has been demonstrated as a critical aspect of knowledge discovery. It is not unreasonable therefore to expect that connecting-the-dots across massive amounts of healthcare data may also lead to new insights that could impact diagnosis, treatment and overall patient care. Of critical importance is the observation that while structured Electronic Medical Records (EMR) are useful sources of health information, it is often the unstructured clinical texts such as progress notes and discharge summaries that contain rich, updated and granular information. Hence, by coupling structured EMR data with data from unstructured clinical texts, more holistic patient records, needed for connecting the dots, can be obtained. Unfortunately, free-text progress notes are fraught with a lack of proper grammatical structure, and contain liberal use of jargon and abbreviations, together with frequent misspellings. While these notes still serve their intended purpose for medical care, automatically extracting semantic information from them is a complex task. Overcoming this complexity could mean that evidence-based support for structured EMR data using unstructured clinical texts, can be provided. In this work therefore, we explore a pattern-based approach for extracting Smoker Semantic Types (SST) from unstructured clinical notes, in order to enable evidence-based resolution of SSTs asserted in structured EMRs using SSTs extracted from unstructured clinical notes. Our findings support the notion that information present in unstructured clinical text can be used to complement structured healthcare data. This is a crucial observation towards creating comprehensive longitudinal patient models for connecting-the-dots and providing better overall patient care.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBMW.2012.6470310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

The ability to connect the dots in structured background knowledge and also across scientific literature has been demonstrated as a critical aspect of knowledge discovery. It is not unreasonable therefore to expect that connecting-the-dots across massive amounts of healthcare data may also lead to new insights that could impact diagnosis, treatment and overall patient care. Of critical importance is the observation that while structured Electronic Medical Records (EMR) are useful sources of health information, it is often the unstructured clinical texts such as progress notes and discharge summaries that contain rich, updated and granular information. Hence, by coupling structured EMR data with data from unstructured clinical texts, more holistic patient records, needed for connecting the dots, can be obtained. Unfortunately, free-text progress notes are fraught with a lack of proper grammatical structure, and contain liberal use of jargon and abbreviations, together with frequent misspellings. While these notes still serve their intended purpose for medical care, automatically extracting semantic information from them is a complex task. Overcoming this complexity could mean that evidence-based support for structured EMR data using unstructured clinical texts, can be provided. In this work therefore, we explore a pattern-based approach for extracting Smoker Semantic Types (SST) from unstructured clinical notes, in order to enable evidence-based resolution of SSTs asserted in structured EMRs using SSTs extracted from unstructured clinical notes. Our findings support the notion that information present in unstructured clinical text can be used to complement structured healthcare data. This is a crucial observation towards creating comprehensive longitudinal patient models for connecting-the-dots and providing better overall patient care.
实现全面的纵向医疗保健数据捕获
连接结构化背景知识和科学文献中的点的能力已被证明是知识发现的一个关键方面。因此,期望将大量医疗保健数据中的点连接起来也可能产生新的见解,从而影响诊断、治疗和整体患者护理,这并非不合理。至关重要的是,虽然结构化电子医疗记录(EMR)是有用的健康信息来源,但通常是非结构化的临床文本,如进度记录和出院摘要,包含丰富的、更新的和细粒度的信息。因此,通过将结构化EMR数据与非结构化临床文本的数据相结合,可以获得连接各个点所需的更全面的患者记录。不幸的是,自由文本进度笔记充满了缺乏适当的语法结构,并且包含大量使用术语和缩写,以及频繁的拼写错误。虽然这些笔记仍然服务于医疗保健的预期目的,但自动从中提取语义信息是一项复杂的任务。克服这种复杂性可能意味着可以使用非结构化临床文本为结构化电子病历数据提供循证支持。因此,在这项工作中,我们探索了一种基于模式的方法,用于从非结构化临床记录中提取吸烟者语义类型(SST),以便使用从非结构化临床记录中提取的SST来实现结构化电子病历中断言的SST的循证解决。我们的研究结果支持这样一种观点,即非结构化临床文本中的信息可以用来补充结构化医疗数据。这是一个重要的观察结果,有助于创建全面的纵向患者模型,将各个点连接起来,提供更好的整体患者护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信