Classification and analysis of text transcription from Thai depression assessment tasks among patients with depression
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Issued Date
2023-03-01
Resource Type
eISSN
19326203
Scopus ID
2-s2.0-85151424895
Pubmed ID
36996118
Journal Title
PLoS ONE
Volume
18
Issue
3 March
Rights Holder(s)
SCOPUS
Bibliographic Citation
PLoS ONE Vol.18 No.3 March (2023)
Suggested Citation
Munthuli A., Pooprasert P., Klangpornkun N., Phienphanich P., Onsuwan C., Jaisin K., Pattanaseri K., Lortrakul J., Tantibundhit C. Classification and analysis of text transcription from Thai depression assessment tasks among patients with depression. PLoS ONE Vol.18 No.3 March (2023). doi:10.1371/journal.pone.0283095 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/82141
Title
Classification and analysis of text transcription from Thai depression assessment tasks among patients with depression
Author's Affiliation
Other Contributor(s)
Abstract
Depression is a serious mental health disorder that poses a major public health concern in Thailand and have a profound impact on individuals’ physical and mental health. In addition, the lack of number to mental health services and limited number of psychiatrists in Thailand make depression particularly challenging to diagnose and treat, leaving many individuals with the condition untreated. Recent studies have explored the use of natural language processing to enable access to the classification of depression, particularly with a trend toward transfer learning from pre-trained language model. In this study, we attempted to evaluate the effectiveness of using XLM-RoBERTa, a pre-trained multi-lingual language model supporting the Thai language, for the classification of depression from a limited set of text transcripts from speech responses. Twelve Thai depression assessment questions were developed to collect text transcripts of speech responses to be used with XLM-RoBERTa in transfer learning. The results of transfer learning with text transcription from speech responses of 80 participants (40 with depression and 40 normal control) showed that when only one question (Q1) of “How are you these days?” was used, the recall, precision, specificity, and accuracy were 82.5%, 84.65, 85.00, and 83.75%, respectively. When utilizing the first three questions from Thai depression assessment tasks (Q1 − Q3), the values increased to 87.50%, 92.11%, 92.50%, and 90.00%, respectively. The local interpretable model explanations were analyzed to determine which words contributed the most to the model’s word cloud visualization. Our findings were consistent with previously published literature and provide similar explanation for clinical settings. It was discovered that the classification model for individuals with depression relied heavily on negative terms such as ‘not,’ ‘sad,’, ‘mood’, ‘suicide’, ‘bad’, and ‘bore’ whereas normal control participants used neutral to positive terms such as ‘recently,’ ‘fine,’, ‘normally’, ‘work’, and ‘working’. The findings of the study suggest that screening for depression can be facilitated by eliciting just three questions from patients with depression, making the process more accessible and less time-consuming while reducing the already huge burden on healthcare workers.