Automated chart review for asthma cohort identification using natural language processing: an exploratory study.
Automated chart review for asthma cohort identification using natural language processing: an exploratory study.
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DOI：
10.1016/j.anai.2013.07.022
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发表时间：
2013-11
影响因子：
5.9
通讯作者：
Juhn, Young J.
中科院分区：
文献类型：
--
作者：
Wu, Stephen T.;Sohn, Sunghwan;Ravikumar, K. E.;Wagholikar, Kavishwar;Jonnalagadda, Siddhartha R.;Liu, Hongfang;Juhn, Young J.
有相当大比例的哮喘儿童推迟了卫生保健提供者对哮喘的诊断。根据已建立的标准进行手动图表审查比直接使用诊断代码更准确，后者往往会低估哮喘患者的身份，但图表审查成本更高，及时性更差。评估哮喘确诊的计算方法的准确性，表征其在电子病历中大规模部署的实用性和可行性。开发了一个自然语言处理(NLP)系统，用于从电子病历的非结构化文本中提取预定的哮喘标准，然后根据这些标准推断哮喘状态。使用手动图表评审作为金标准，哮喘状态(是与否)和识别日期(“是”哮喘状态的第一个日期)由NLP系统确定。研究对象为一组年龄小于4岁(平均2.0岁，标准差1.03岁)的儿童(n=112，高加索人占84%，女孩占49%)。NLP方法诊断哮喘的敏感度、特异度、阳性预测值、阴性预测值和中位延迟分别为84.6%、96.5%、88.0%、95.4%和0个月；而诊断代码分别为30.8%、93.2%、57.1%、82.2%和2.3个月。使用NLP从电子病历中自动确定哮喘是可行的，而且比诊断代码等传统方法更准确。考虑到劳动密集型人工记录审查的困难，应考虑采用NLP方法来确定哮喘，以改进临床护理和研究，特别是在大规模工作中。
A significant proportion of children with asthma have delayed diagnosis of asthma by health care providers. Manual chart review according to established criteria is more accurate than directly using diagnosis codes, which tend to under-identify asthmatics, but chart reviews are more costly and less timely. To evaluate the accuracy of a computational approach to asthma ascertainment, characterizing its utility and feasibility toward large-scale deployment in electronic medical records. A natural language processing (NLP) system was developed for extracting predetermined criteria for asthma from unstructured text in electronic medical records and then inferring asthma status based on these criteria. Using manual chart reviews as a gold standard, asthma status (yes vs no) and identification date (first date of a “yes” asthma status) were determined by the NLP system. Patients were a group of children (n =112, 84% Caucasian, 49% girls) younger than 4 years (mean 2.0 years, standard deviation 1.03 years) who participated in previous studies. The NLP approach to asthma ascertainment showed sensitivity, specificity, positive predictive value, negative predictive value, and median delay in diagnosis of 84.6%, 96.5%, 88.0%, 95.4%, and 0 months, respectively; this compared favorably with diagnosis codes, at 30.8%, 93.2%, 57.1%, 82.2%, and 2.3 months, respectively. Automated asthma ascertainment from electronic medical records using NLP is feasible and more accurate than traditional approaches such as diagnosis codes. Considering the difficulty of labor-intensive manual record review, NLP approaches for asthma ascertainment should be considered for improving clinical care and research, especially in large-scale efforts.
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影响因子：
8
作者：
Daley, MF;Barrow, J;Kempe, A
通讯作者：
Kempe, A
影响因子：
1.9
作者：
Donahue, JG;Weiss, ST;Platt, R
通讯作者：
Platt, R
影响因子：
13.6
作者：
Grassi, M;Villani, S;Marinoni, A
通讯作者：
Marinoni, A
影响因子：
14.2
作者：
Juhn, Young J.;Kita, Hirohito;Jacobson, Robert M.
通讯作者：
Jacobson, Robert M.
影响因子：
2.8
作者：
Lynch, Brian A.;Van Norman, Chelsey A.;Juhn, Young J.
通讯作者：
Juhn, Young J.