Automated chart review utilizing natural language processing algorithm for asthma predictive index.
Automated chart review utilizing natural language processing algorithm for asthma predictive index.
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DOI：
10.1186/s12890-018-0593-9
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发表时间：
2018-02-13
影响因子：
3.1
通讯作者：
Juhn YJ
中科院分区：
文献类型：
--
作者：
Kaur H;Sohn S;Wi CI;Ryu E;Park MA;Bachman K;Kita H;Croghan I;Castro-Rodriguez JA;Voge GA;Liu H;Juhn YJ
到目前为止，还没有开发出从电子健康记录(EHR)中自动提取符合哮喘预测指数(API)标准的患者的算法。我们的目标是开发和验证一种自然语言处理(NLP)算法来识别符合API标准的患者。这是一项嵌套在明尼苏达州奥姆斯特德县出生队列研究中的横断面研究。以以API标准为基础的手工复查图表确定哮喘状态为金标准。Nlp-api是在训练队列(n = 87)上开发的，并在测试队列(n = 427)上验证。标准效度通过NLP算法的敏感性、特异性、阳性预测值和阴性预测值来衡量，而不是手工查阅哮喘状态图表。结构效度是通过NLP-API定义的哮喘状态与已知哮喘危险因素的关联来确定的。在测试队列中符合条件的427名受试者中，48%是男性，74%是白人。中位年龄为5.3岁(四分位数范围3.6-6.8)。NLP-API有35例(8%)有哮喘史，而抽取器有36例(8%)，两种方法都有31例。NLP-API预测哮喘病情的敏感性为86%，特异性为98%，阳性预测值为88%，阴性预测值为98%。NLP和手动图检查的哮喘状况与已知的哮喘危险因素显著相关，如过敏性鼻炎、湿疹、哮喘家族史和母亲孕期吸烟史(p值<； 0.05)。母亲吸烟[优势比：4.4，95%可信区间1.8-10.7]与NLP-API和抽取器确定的哮喘状态相关，两次综述的影响大小相似，分别为4.4和4.2。NLP-API能够确定从EHR挖掘的儿童中的哮喘状况，并有潜力通过人口管理和大规模研究来加强哮喘护理和研究，以确定符合API标准的儿童。
Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language processing (NLP) algorithm to identify patients that meet API criteria. This is a cross-sectional study nested in a birth cohort study in Olmsted County, MN. Asthma status ascertained by manual chart review based on API criteria served as gold standard. NLP-API was developed on a training cohort (n = 87) and validated on a test cohort (n = 427). Criterion validity was measured by sensitivity, specificity, positive predictive value and negative predictive value of the NLP algorithm against manual chart review for asthma status. Construct validity was determined by associations of asthma status defined by NLP-API with known risk factors for asthma. Among the eligible 427 subjects of the test cohort, 48% were males and 74% were White. Median age was 5.3 years (interquartile range 3.6–6.8). 35 (8%) had a history of asthma by NLP-API vs. 36 (8%) by abstractor with 31 by both approaches. NLP-API predicted asthma status with sensitivity 86%, specificity 98%, positive predictive value 88%, negative predictive value 98%. Asthma status by both NLP and manual chart review were significantly associated with the known asthma risk factors, such as history of allergic rhinitis, eczema, family history of asthma, and maternal history of smoking during pregnancy (p value < 0.05). Maternal smoking [odds ratio: 4.4, 95% confidence interval 1.8–10.7] was associated with asthma status determined by NLP-API and abstractor, and the effect sizes were similar between the reviews with 4.4 vs 4.2 respectively. NLP-API was able to ascertain asthma status in children mining from EHR and has a potential to enhance asthma care and research through population management and large-scale studies when identifying children who meet API criteria.
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影响因子：
158.5
作者：
Ducharme, Francine M.;Lemire, Chantal;Platt, Robert W.
通讯作者：
Platt, Robert W.
影响因子：
19.7
作者：
Hripcsak, G;Austin, JHM;Friedman, C
通讯作者：
Friedman, C
影响因子：
120.7
作者：
Murff, Harvey J.;FitzHenry, Fern;Speroff, Theodore
通讯作者：
Speroff, Theodore
影响因子：
168.9
作者：
Asher, M. Innes;Montefort, Stephen;Williams, Hywel
通讯作者：
Williams, Hywel
DOI：
10.1016/j.jaci.2010.11.015
发表时间：
2011-02
期刊：
The Journal of allergy and clinical immunology
影响因子：
--
作者：
Fitzpatrick AM;Teague WG;Meyers DA;Peters SP;Li X;Li H;Wenzel SE;Aujla S;Castro M;Bacharier LB;Gaston BM;Bleecker ER;Moore WC;National Institutes of Health/National Heart, Lung, and Blood Institute Severe Asthma Research Program
通讯作者：
National Institutes of Health/National Heart, Lung, and Blood Institute Severe Asthma Research Program