LATTE: A knowledge-based method to normalize various expressions of laboratory test results in free text of Chinese electronic health records
LATTE: A knowledge-based method to normalize various expressions of laboratory test results in free text of Chinese electronic health records
复制标题
LATTE：一种基于知识的方法，规范中国电子健康记录自由文本中实验室检测结果的各种表达
DOI：
10.1016/j.jbi.2019.103372
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发表时间：
2020-02-01
影响因子：
4.5
通讯作者：
Jiang, Taijiao
中科院分区：
文献类型：
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作者：
Jiang, Kun;Yang, Tao;Jiang, Taijiao
背景资料：大量的临床信息隐藏在电子健康记录（EHR）的自由文本中，将临床信息转换为机器可理解的形式对于EHR的二次使用至关重要。实验室检查结果作为临床信息的重要类型之一，在电子病历的自由文本中以各种不同的格式书写。这给电子病历的数据集成和利用带来了很大的困难。因此，开发技术规范化的不同表达的实验室测试结果的自由文本是必不可少的二次使用EHRs.Methods：在这项研究中，我们开发了一种基于知识的方法命名为LATTE（transforming lab test results），它可以将各种表达的实验室测试结果转化为一个规范化的和机器可理解的格式。首先采用基于词典的方法识别实验室检测结果中的待测物，然后设计一系列规则来检测待测物的样本、测量值、测量单位、结论短语和采样因子等相关信息。我们通过理解结论性短语的含义或将其测量值与适当的正常范围进行比较来确定测试结果是正常还是异常。最后，我们将实验室测试结果的各种表达（无论是数字形式还是文本形式）转换为标准化形式，即“被测物-分析物-异常”。使用这种方法，具有相同类型的异常的实验室测试将具有相同的表示，无论它是在自由文本中提到的方式。结果：LATTE开发和优化的训练集，包括8894实验室测试结果从756 EHRs，并评估测试集，包括3740实验室测试结果从210 EHRs。与专家的注释相比，LATTE在训练集上的精确度为0.936，召回率为0.897，F1得分为0.916，在测试集上的精确度为0.892，召回率为0.843，F1得分为0.867。对于测试集中至少有两种不同表达形式的223项实验室测试，LATTE将85.7%（2870/3350）的实验室测试结果转换为标准化形式。此外，LATTE取得了F1得分超过0.8的EHR从18个不同的21个医院部门，表明其泛化能力在规范化实验室检查结果。结论：总之，LATTE是一种有效的方法，规范化的各种表达的实验室检查结果在自由文本的EHR。LATTE将促进基于EHR的应用程序，如队列查询，患者聚类和机器学习。可用性：LATTE可在GitHub上免费下载（https：//github.com/denglizong/LATTE）。
Background: A wealth of clinical information is buried in free text of electronic health records (EHR), and converting clinical information to machine-understandable form is crucial for the secondary use of EHRs. Laboratory test results, as one of the most important types of clinical information, are written in various styles in free text of EHRs. This has brought great difficulties for data integration and utilization of EHRs. Therefore, developing technology to normalize different expressions of laboratory test results in free text is indispensable for the secondary use of EHRs.Methods: In this study, we developed a knowledge-based method named LATTE (transforming lab test results), which could transform various expressions of laboratory test results into a normalized and machine-understandable format. We first identified the analyte of a laboratory test result with a dictionary-based method and then designed a series of rules to detect information associated with the analyte, including its specimen, measured value, unit of measure, conclusive phrase and sampling factor. We determined whether a test result is normal or abnormal by understanding the meaning of conclusive phrases or by comparing its measured value with an appropriate normal range. Finally, we converted various expressions of laboratory test results, either in numeric or textual form, into a normalized form as "specimen-analyte-abnormality". With this method, a laboratory test with the same type of abnormality would have the same representation, regardless of the way that it is mentioned in free text.Results: LATTE was developed and optimized on a training set including 8894 laboratory test results from 756 EHRs, and evaluated on a test set including 3740 laboratory test results from 210 EHRs. Compared to experts' annotations, LATTE achieved a precision of 0.936, a recall of 0.897 and an F1 score of 0.916 on the training set, and a precision of 0.892, a recall of 0.843 and an F1 score of 0.867 on the test set. For 223 laboratory tests with at least two different expression forms in the test set, LATTE transformed 85.7% (2870/3350) of laboratory test results into a normalized form. Besides, LATTE achieved F1 scores above 0.8 for EHRs from 18 of 21 different hospital departments, indicating its generalization capabilities in normalizing laboratory test results.Conclusion: In conclusion, LATTE is an effective method for normalizing various expressions of laboratory test results in free text of EHRs. LATTE will facilitate EHR-based applications such as cohort querying, patient clustering and machine learning.Availability: LATTE is freely available for download on GitHub (https://github.com/denglizong/LATTE).