Empirical advances with text mining of electronic health records.
Empirical advances with text mining of electronic health records.
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电子健康记录的文本挖掘经验进步。
DOI：
10.1186/s12911-017-0519-0
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发表时间：
2017-08-22
影响因子：
3.5
通讯作者：
Josseran L
中科院分区：
文献类型：
--
作者：
Delespierre T;Denormandie P;Bar-Hen A;Josseran L
Korian是一家私人集团，专门为老年人和受抚养人提供医疗设施。2010年建立的专业数据仓库（DWH）托管了所有居民的数据。在此信息系统（IS）中，临床叙述（CN）仅由医务人员用作居民护理链接工具。本研究的目的是表明，通过定性和定量的文本分析一个相对较小的物理治疗和定义明确的CN样本，有可能建立一个物理治疗语料库，并通过这个过程中，产生一个新的知识体系，通过添加相关信息来描述居民的护理和生活。通过标准查询语言（SQL）使用LIKE函数和通配符提取有意义的单词以执行模式匹配，然后使用R®包进行文本挖掘和单词云。另一个步骤涉及主成分和多重对应分析，加上对同一居民样本的聚类以及使用测量居民护理水平需求的健康模型对其他健康数据的聚类。通过结合这些技术，物理治疗可以通过构建关键字列表来表征，并建立居民的健康特征。喂养缺陷或健康离群人群可以检测到，物理治疗居民的数据和他们的健康数据相匹配，健康状况的差异显示了物理治疗叙述的定性和定量差异。这个文本实验使用文本过程中的两个阶段表明，文本挖掘和数据挖掘技术提供了方便的工具，以提高居民的健康和医疗质量，通过添加新的，简单的，可用的数据的电子健康记录（EHR）。当与规范化的物理治疗问题列表一起使用时，通过信息提取（IE）、命名实体识别（NER）和数据挖掘（DM）的文本挖掘可以提供真实的优势来描述医疗保健，添加新的医疗材料并帮助将EHR系统集成到卫生工作人员的工作环境中。本文的在线版本（doi：10.1186/s12911-017-0519-0）包含补充材料，可供授权用户使用。
Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse (DWH) established in 2010 hosts all of the residents’ data. Inside this information system (IS), clinical narratives (CNs) were used only by medical staff as a residents’ care linking tool. The objective of this study was to show that, through qualitative and quantitative textual analysis of a relatively small physiotherapy and well-defined CN sample, it was possible to build a physiotherapy corpus and, through this process, generate a new body of knowledge by adding relevant information to describe the residents’ care and lives. Meaningful words were extracted through Standard Query Language (SQL) with the LIKE function and wildcards to perform pattern matching, followed by text mining and a word cloud using R® packages. Another step involved principal components and multiple correspondence analyses, plus clustering on the same residents’ sample as well as on other health data using a health model measuring the residents’ care level needs. By combining these techniques, physiotherapy treatments could be characterized by a list of constructed keywords, and the residents’ health characteristics were built. Feeding defects or health outlier groups could be detected, physiotherapy residents’ data and their health data were matched, and differences in health situations showed qualitative and quantitative differences in physiotherapy narratives. This textual experiment using a textual process in two stages showed that text mining and data mining techniques provide convenient tools to improve residents’ health and quality of care by adding new, simple, useable data to the electronic health record (EHR). When used with a normalized physiotherapy problem list, text mining through information extraction (IE), named entity recognition (NER) and data mining (DM) can provide a real advantage to describe health care, adding new medical material and helping to integrate the EHR system into the health staff work environment. The online version of this article (doi:10.1186/s12911-017-0519-0) contains supplementary material, which is available to authorized users.
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影响因子：
9.3
作者：
McGinn CA;Grenier S;Duplantie J;Shaw N;Sicotte C;Mathieu L;Leduc Y;Légaré F;Gagnon MP
通讯作者：
Gagnon MP
影响因子：
1
作者：
Ayele, Dawit;Zewotir, Temesgen;Mwambi, Henry
通讯作者：
Mwambi, Henry
DOI：
10.2174/1874431101307010034
发表时间：
2013-01-01
期刊：
The open medical informatics journal
影响因子：
--
作者：
Genes, N;Chandra, D;Baumlin, K
通讯作者：
Baumlin, K
影响因子：
4.1
作者：
Leemrijse, Chantal J;de Boer, Marike E;Dekker, Joost
通讯作者：
Dekker, Joost
影响因子：
3.5
作者：
Holmes C;Brown M;Hilaire DS;Wright A
通讯作者：
Wright A