Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan R.O.C; College of Medicine, National Taiwan University, Taipei, Taiwan R.O.C.
Taipei City Psychiatric Center, Taipei City Hospital, Taipei, Taiwan R.O.C; Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taiwan R.O.C.
J Affect Disord. 2020 Jan 1;260:617-623. doi: 10.1016/j.jad.2019.09.044. Epub 2019 Sep 11.
Many studies have used Taiwan's National Health Insurance Research database (NHIRD) to conduct psychiatric research. However, the accuracy of the diagnostic codes for psychiatric disorders in NHIRD is not validated, and the symptom profiles are not available either. This study aimed to evaluate the accuracy of diagnostic codes and use text mining to extract symptom profile and functional impairment from electronic health records (EHRs) to overcome the above research limitations.
A total of 500 discharge notes were randomly selected from a medical center's database. Three annotators reviewed the notes to establish gold standards. The accuracy of diagnostic codes for major psychiatric illness was evaluated. Text mining approaches were applied to extract depressive symptoms and function profiles and to identify patients with major depressive disorder.
The accuracy of the diagnostic code for major depressive disorder, schizophrenia, and dementia was acceptable but that of bipolar disorder and minor depression was less satisfactory. The performance of text mining approach to recognize depressive symptoms is satisfactory; however, the recall for functional impairment is lower resulting in lower F-scores of 0.774-0.753. Using the text mining approach to identify major depressive disorder, the recall was 0.85 but precision was only 0.69.
The accuracy of the diagnostic code for major depressive disorder in discharge notes was generally acceptable. This finding supports the utilization of psychiatric diagnoses in claims databases. The application of text mining to EHRs might help in overcoming current limitations in research using claims databases.
许多研究利用台湾全民健康保险研究数据库（NHIRD）进行精神科研究。然而，NHIRD 中精神障碍诊断代码的准确性未经验证，症状特征也不可用。本研究旨在评估诊断代码的准确性，并利用文本挖掘从电子健康记录（EHR）中提取症状特征和功能障碍，以克服上述研究限制。
从一家医学中心的数据库中随机抽取 500 份出院记录。三名注释员对记录进行审查，以建立黄金标准。评估了主要精神疾病诊断代码的准确性。应用文本挖掘方法提取抑郁症状和功能特征，并识别出患有重度抑郁症的患者。
重度抑郁症、精神分裂症和痴呆症的诊断代码准确性可以接受，但双相情感障碍和轻度抑郁症的准确性则不太令人满意。文本挖掘方法识别抑郁症状的性能令人满意；然而，对功能障碍的召回率较低，导致 F 分数较低，为 0.774-0.753。使用文本挖掘方法识别重度抑郁症，召回率为 0.85，但精度仅为 0.69。
出院记录中重度抑郁症诊断代码的准确性总体上可以接受。这一发现支持在理赔数据库中使用精神科诊断。将文本挖掘应用于 EHR 可能有助于克服使用理赔数据库进行研究的当前限制。
