Performances of Depression Detection through Deep Learning-based Natural Language Processing to Mandarin Chinese Medical Records: Comparison between Civilian and Military Populations

Tai-Yu Chen; Hsuan-Te Chu; Yueh-Ming Tai; Szu-Nian Yang

doi:10.4103/TPSY.TPSY_9_22

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医药卫生 > 社会医学

DOI： 10.29478/TJP

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Performances of Depression Detection through Deep Learning-based Natural Language Processing to Mandarin Chinese Medical Records: Comparison between Civilian and Military Populations

Tai-Yu Chen ； Hsuan-Te Chu ； Yueh-Ming Tai ； Szu-Nian Yang

台湾精神医学； 36 卷 1 期 (2022 / 03 / 01) ， P32 - 38

英文 DOI： 10.4103/TPSY.TPSY_9_22

area under curve (AUC) ； artificial intelligence ； bidirectional decoder representations from transformers ； receiver operating characteristic (ROC)

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摘要 │ 参考文献 (22) │ 被引用文献 (2) │ 国际计量

摘要〈TOP〉

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 (BERT_(general)) and of civilian samples (BERT_(civilian)) and of military samples (BERT_(military)) 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 BERT_(general) 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). BERT_(general) 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 BERT_(military) 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.

参考文献 ( 22 ) 〈TOP〉

Ahmad, I,Pothuganti, K(2020).Analysis of different convolution neural network models to diagnose Alzheimer’s disease.Mater Today Proc,37,2800-2812.
Ariyarathne, G,De Silva, S,Dayarathna, S(2020).ADHD identification using convolutional neural network with seed-based approach for fMRI data.Proceedings of the 2020 9th International Conference on Software and Computer Applications,Chicago, Illinois, USA:
Colic, S,He, JC,Richardson, JD(2021).A machine learning approach to identification of self-harm and suicidal ideation among military and police veterans.J Mil Veteran Fam Health,8,e20210035-e20210043.
Core Team R(2013).Core Team R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2013..
Dai, HJ,Su, CH,Lee, YQ,Das, S,Blacker, D,Smoller, JW(2020).Deep learning-based natural language processing for screening psychiatric patients.Front Psychiatry,11,533949.

被引用文献 ( 2 ) 〈TOP〉

(2024).Performance of Artificial Intelligence Models (Bidirectional Encoder Representations from Transformers + TextCNN) in Detecting Eight Psychiatric Diagnoses from Unstructured Texts Chinese Electronic Medical Records.台湾精神医学,38(3),120-127.
(2024).Performances of Large Language Models in Detecting Psychiatric Diagnoses from Chinese Electronic Medical Records: Comparisons between GPT-3.5, GPT-4, and GPT-4o.台湾精神医学,38(3),134-141.

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