A review of approaches to identifying patient phenotype cohorts using electronic health records.
A review of approaches to identifying patient phenotype cohorts using electronic health records.
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DOI：
10.1136/amiajnl-2013-001935
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发表时间：
2014-03
期刊：
影响因子：
--
通讯作者：
Lai AM
中科院分区：
文献类型：
--
作者：
Shivade C;Raghavan P;Fosler-Lussier E;Embi PJ;Elhadad N;Johnson SB;Lai AM
总结描述旨在自动识别具有共同表型的患者的方法的文献。我们对描述用于识别特定表型患者队列的系统或报告技术的研究进行了综述。对过去3 年内发表在(1)《美国医学信息学协会杂志》、(2)《生物医学信息学杂志》、(3)《美国医学信息学协会年会论文集》和(4)《临床研究信息学会议论文集》的每一篇全文文章都进行了评估。只收录了使用自动化技术的文章。有97篇文章符合我们的入选标准。46项使用了基于自然语言处理(NLP)的技术，24项描述了基于规则的系统，41项使用了统计分析、数据挖掘或机器学习技术，22项描述了混合系统。9篇文章描述了为确定患者的队列资格而开发的大规模系统的体系结构。我们观察到，与使用电子医疗记录进行队列识别相关的研究数量有所增加。与基于规则的系统相比，统计分析或机器学习，然后是自然语言处理技术，多年来越来越受欢迎。有多种方法可以将患者分类为特定的表型。使用了不同的技术和数据源，并报告了各自机构的数据集表现良好。然而，没有一个系统全面利用电子病历来解决其所有已知的弱点。
To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype. We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included. Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients. We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems. There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.
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DOI：
10.1136/amiajnl-2011-000115
发表时间：
2012-06-01
影响因子：
6.4
作者：
Ferranti, Jeffrey M.;Gilbert, William;Horvath, Monica M.
通讯作者：
Horvath, Monica M.
影响因子：
2.9
作者：
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通讯作者：
Bain, C.
DOI：
10.1177/106286069901400607
发表时间：
1999-11-01
期刊：
American journal of medical quality : the official journal of the American College of Medical Quality
影响因子：
--
作者：
Hebert, P L;Geiss, L S;McBean, A M
通讯作者：
McBean, A M
影响因子：
3.7
作者：
DeLisle S;South B;Anthony JA;Kalp E;Gundlapallli A;Curriero FC;Glass GE;Samore M;Perl TM
通讯作者：
Perl TM
DOI：
10.1136/amiajnl-2011-000535
发表时间：
2012-09-01
影响因子：
6.4
作者：
Garvin, Jennifer H.;DuVall, Scott L.;Goldstein, Mary K.
通讯作者：
Goldstein, Mary K.