Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department.
Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department.
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DOI：
10.1136/amiajnl-2013-001962
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发表时间：
2013-12
期刊：
影响因子：
--
通讯作者：
Solti I
中科院分区：
文献类型：
--
作者：
Deleger L;Brodzinski H;Zhai H;Li Q;Lingren T;Kirkendall ES;Alessandrini E;Solti I
评估一种基于自然语言处理（NLP）和机器学习的自动化方法，通过分析电子健康记录（EHR）的内容对腹痛患者进行风险分层。我们分析了随机抽取的2100例小儿急诊科（艾德）腹痛患者的EHR，包括所有最终诊断为阑尾炎的患者。我们开发了一个自动化系统，从艾德医生记录和实验室值中提取相关元素，并根据儿科阑尾炎评分自动分配急性阑尾炎的风险类别（高，可疑或低）。我们根据手动创建的黄金标准（由艾德医生进行图表审查）评估了系统的召回率、特异性和精确度。该系统实现了风险分类的平均F值为0.867（召回率为0.869，精确度为0.863），与医生专家相当。召回率/精确率在低风险类别中为0.897/0.952，在高风险类别中为0.855/0.886，在可疑风险类别中为0.854/0.766。在艾德访视的前4小时内，系统需要作为输入以获得高F测量值的信息可用。 基于EHR内容（包括来自临床笔记的信息）的自动阑尾炎风险分类显示出与医生图表审查员相当的性能，如通过其注释者间协议所测量的，并且代表了一种有前途的计算机化决策支持新方法，以促进循证医学在护理点的应用。
To evaluate a proposed natural language processing (NLP) and machine-learning based automated method to risk stratify abdominal pain patients by analyzing the content of the electronic health record (EHR). We analyzed the EHRs of a random sample of 2100 pediatric emergency department (ED) patients with abdominal pain, including all with a final diagnosis of appendicitis. We developed an automated system to extract relevant elements from ED physician notes and lab values and to automatically assign a risk category for acute appendicitis (high, equivocal, or low), based on the Pediatric Appendicitis Score. We evaluated the performance of the system against a manually created gold standard (chart reviews by ED physicians) for recall, specificity, and precision. The system achieved an average F-measure of 0.867 (0.869 recall and 0.863 precision) for risk classification, which was comparable to physician experts. Recall/precision were 0.897/0.952 in the low-risk category, 0.855/0.886 in the high-risk category, and 0.854/0.766 in the equivocal-risk category. The information that the system required as input to achieve high F-measure was available within the first 4 h of the ED visit. Automated appendicitis risk categorization based on EHR content, including information from clinical notes, shows comparable performance to physician chart reviewers as measured by their inter-annotator agreement and represents a promising new approach for computerized decision support to promote application of evidence-based medicine at the point of care.
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临床决策支持具有自动化文本处理，用于宫颈癌筛查。
DOI：
10.1136/amiajnl-2012-000820
发表时间：
2012-09
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Wagholikar KB;MacLaughlin KL;Henry MR;Greenes RA;Hankey RA;Liu H;Chaudhry R
通讯作者：
Chaudhry R
DOI：
10.1136/jamia.2009.001560
发表时间：
2010-09-01
影响因子：
6.4
作者：
Savova, Guergana K.;Masanz, James J.;Chute, Christopher G.
通讯作者：
Chute, Christopher G.
影响因子：
8
作者：
Kharbanda, Anupam B.;Stevenson, Michelle D.;Dayan, Peter S.
通讯作者：
Dayan, Peter S.
影响因子：
8
作者：
Fahimi, Jahan;Herring, Andrew;Alter, Harrison
通讯作者：
Alter, Harrison
影响因子：
4.4
作者：
Kharbanda, Anupam B.;Fishman, Steven J.;Bachur, Richard G.
通讯作者：
Bachur, Richard G.