Bias of Inaccurate Disease Mentions in Electronic Health Record-based Phenotyping
Bias of Inaccurate Disease Mentions in Electronic Health Record-based Phenotyping
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DOI：
10.1016/j.ijmedinf.2018.12.004
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发表时间：
2019-04-01
影响因子：
4.9
通讯作者：
Ohe, Kazuhiko
中科院分区：
文献类型：
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作者：
Kagawa, Rina;Shinohara, Emiko;Ohe, Kazuhiko
目标：基于电子健康记录 (EHR) 的表型分析是一种利用 EHR 数据识别诊断患有特定疾病的患者的自动化技术。然而，基于 EHR 的表型分析很难实现令人满意的高性能，因为临床记录中包含的疾病提及最终意味着除患者诊断之外的其他内容（例如鉴别诊断或筛查）。我们的目标是量化此类疾病提及对基于 EHR 的表型分析性能的影响。方法：医生手动审查 4,430 名患者的 487,300 份临床记录中的疾病提及是否表明患者的疾病。特别关注的是那些并不表示患者诊断的疾病提及，即使它们在同一句子中没有任何句法修饰语或指示符。然后根据临床记录是否包含此类疾病提及对患者进行分类。结果：在临床记录包含疾病提及而没有任何修饰语或指示符的患者中，疾病提及表明患者诊断的患者比例为78.1%（平均）。该值可以解释为疾病提及的偏差，通过从临床记录中提取疾病提及，并不表示患者对基于 EHR 表型分析精度的诊断。结论：本研究量化了由于疾病提及而发生的偏差，这些疾病提及错误地表示了四种数据集类型中基于 EHR 表型分析精度值的患者诊断。这项研究的结果将帮助不同研究环境中具有不同可用数据类型的研究人员。
Objectives: Electronic health record (EHR)-based phenotyping is an automated technique for identifying patients diagnosed with a particular disease using EHR data. However, EHR-based phenotyping has difficulties in achieving satisfactorily high performance because clinical notes include disease mentions that ultimately signify something other than the patient's diagnosis (such as differential diagnosis or screening). Our objective is to quantify the influence of such disease mentions on EHR-based phenotyping performance.Methods: Physicians manually reviewed whether the disease mentions indicated the patients' diseases in 487,300 clinical notes of 4,430 patients. Particular focus was placed on disease mentions that did not signify the patient's diagnosis even though they did not have any syntactic modifier or indicator in the same sentences. Patients were then classified according to whether their clinical notes included such disease mentions.Results: Among the patients whose clinical notes included disease mentions without any modifier or indicator, the proportion of patients whose disease mentions signified the patients' diagnosis was 78.1% (on average). This value can be interpreted as the bias of disease mentions that did not signify the patient's diagnosis on the precision of EHR-based phenotyping by extracting disease mentions from clinical notes.Conclusion: This study quantified the bias occurred owing to disease mentions that incorrectly signify a patient's diagnosis in the value of precision of EHR-based phenotyping from four dataset types. The results of this study will help researchers in diverse research environments with different available data types.