SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research.
SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research.
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DOI：
10.1093/jamia/ocx160
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发表时间：
2018-05-01
期刊：
影响因子：
--
通讯作者：
Dobson RJB
中科院分区：
文献类型：
--
作者：
Wu H;Toti G;Morley KI;Ibrahim ZM;Folarin A;Jackson R;Kartoglu I;Agrawal A;Stringer C;Gale D;Gorrell G;Roberts A;Broadbent M;Stewart R;Dobson RJB
解锁电子健康记录（EHR）的结构化和非结构化组件中包含的数据有可能为二次研究使用，生成可操作的医学见解，医院管理和试验招募提供数据的阶跃变化。为了实现这一目标，我们实现了SemEHR，一个用于EHR的开源语义搜索和分析工具。SemEHR实现了一个通用的信息提取（IE）和检索基础设施，通过识别上下文提到的广泛的生物医学概念EHR。自然语言处理注释在患者级别进一步组装，并使用EHR特定的知识进行扩展，以生成每个患者的时间轴。语义数据通过基于本体的搜索和分析接口提供服务。SemEHR已在英国多家医院部署，包括临床记录交互式搜索，这是英国南部伦敦EHR的匿名复制品，以及欧洲最大的心理健康服务提供商之一Maudsley National Health Service Foundation Trust。在2项基于临床记录交互式搜索的研究中，SemEHR在识别真阳性患者方面实现了93%（丙型肝炎）和99%（HIV）的F测量结果。在伦敦的国王学院医院，作为CogStack项目（github.com/cogstack）的一部分，SemEHR被用于招募患者加入英国卫生部10万基因组项目（genomicsengland.co.uk）。验证研究表明，该工具可以验证以前招募的病例，并且在搜索表型方面非常快;招募标准检查的时间从几天减少到几分钟。经开放式重症监护EHR数据（Medical Information Mart for Intensive Care III）验证，SemEHR提取的生命体征准确率约为97%。多个案例研究的结果证明了SemEHR的效率：在某些情况下，数周或数月的工作可以在数小时或数分钟内完成。SemEHR提供了更全面的患者视图，与面向研究的定制IE系统相比，带来了更多意想不到的洞察力。SemEHR是开源的，可在https://github.com/CogStack/SemEHR上获得。
Unlocking the data contained within both structured and unstructured components of electronic health records (EHRs) has the potential to provide a step change in data available for secondary research use, generation of actionable medical insights, hospital management, and trial recruitment. To achieve this, we implemented SemEHR, an open source semantic search and analytics tool for EHRs. SemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualized mentions of a wide range of biomedical concepts within EHRs. Natural language processing annotations are further assembled at the patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data are serviced via ontology-based search and analytics interfaces. SemEHR has been deployed at a number of UK hospitals, including the Clinical Record Interactive Search, an anonymized replica of the EHR of the UK South London and Maudsley National Health Service Foundation Trust, one of Europe’s largest providers of mental health services. In 2 Clinical Record Interactive Search–based studies, SemEHR achieved 93% (hepatitis C) and 99% (HIV) F-measure results in identifying true positive patients. At King’s College Hospital in London, as part of the CogStack program (github.com/cogstack), SemEHR is being used to recruit patients into the UK Department of Health 100 000 Genomes Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast at searching phenotypes; time for recruitment criteria checking was reduced from days to minutes. Validated on open intensive care EHR data, Medical Information Mart for Intensive Care III, the vital signs extracted by SemEHR can achieve around 97% accuracy. Results from the multiple case studies demonstrate SemEHR’s efficiency: weeks or months of work can be done within hours or minutes in some cases. SemEHR provides a more comprehensive view of patients, bringing in more and unexpected insight compared to study-oriented bespoke IE systems. SemEHR is open source, available at https://github.com/CogStack/SemEHR.
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DOI：
10.1136/amiajnl-2011-000536
发表时间：
2012-06-01
影响因子：
6.4
作者：
Mathias, Jason S.;Gossett, Dana;Baker, David W.
通讯作者：
Baker, David W.
影响因子：
4.4
作者：
Stewart, Robert;Soremekun, Mishael;Lovestone, Simon
通讯作者：
Lovestone, Simon
影响因子：
7.4
作者：
Moseley, Edward T.;Hsu, Douglas J.;Celi, Leo Anthony
通讯作者：
Celi, Leo Anthony
影响因子：
4
作者：
Bilal U;Díez J;Alfayate S;Gullón P;Del Cura I;Escobar F;Sandín M;Franco M;HHH Research Group
通讯作者：
HHH Research Group
影响因子：
9.8
作者：
Johnson AE;Pollard TJ;Shen L;Lehman LW;Feng M;Ghassemi M;Moody B;Szolovits P;Celi LA;Mark RG
通讯作者：
Mark RG