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ORIGINAL ARTICLE
Year : 2022  |  Volume : 36  |  Issue : 1  |  Page : 32-38

Performances of depression detection through deep learning-based natural language processing to mandarin chinese medical records: Comparison between civilian and military populations


Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical Center; Military Suicide Prevention Center, Taipei, Taiwan

Correspondence Address:
Yueh- Ming Tai
No. 60, Shin-Ming Road, Beitou District, Taipei 112
Taiwan
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/TPSY.TPSY_9_22

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Objective: A certain portion of patients with depression is under-diagnosed and has attracted the attention in the field of natural language processing (NLP). In this study, we intended to explore the feasibility of transferring unstructured textual records into a screening tool to early detect depression. Methods: We recruited 22,355 medical records in Mandarin traditional Chinese from the psychiatry emergency department of a military psychiatry center from 2004 to 2019. We preprocessed all the context of present illness histories as corpus and the presence of clinical diagnoses of depression as an outcome. A state-of-the-art NLP model was developed based on a pretrained bidirectional encoder representation from transformers (BERT) model along with several convolutional neural network (CNN) and trained by the training set (80% of original data) of total samples (BERTgeneral) and of civilian samples (BERTcivilian) and of military samples (BERTmilitary) independently. The receiver operating characteristic (ROC) and area under curve (AUC) of three trained models were compared for predicting depression for the test dataset (20% of original data) of general and specific samples. Results: The experimental results demonstrated excellent performance of BERTgeneral for general samples (AUC = 0.93, sensitivity = 0.817, specificity = 0.920 for optimal cut-off point) and civilian sample (AUC = 0.91, sensitivity = 0.851, specificity = 0.851 for optimal cut-off point). BERTgeneral showed a significant underperformance of for military samples (AUC = 0.79, sensitivity = 0.712, specificity = 0.732, p < 0.05 for optimal cut-off point). That of BERTmilitary was slight higher (AUC = 0.82, sensitivity = 0.708, specificity = 0.786 for optimal cut-off point) for military samples. Conclusion: This study showed the feasibility of applying deep learning technique as a depression-detection assistant tool in Mandarin Chinese medical records. However, the subjects' specific situation, e.g., military status, is warranted for further investigation.


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