Domain Knowledge Enhanced Text Mining for Identifying Mental Disorder Patterns
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Conference Proceeding
- Citation:
- 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), 2023, 00, pp. 1-10
- Issue Date:
- 2023-10-16
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Domain_Knowledge_Enhanced_Text_Mining_for_Identifying_Mental_Disorder_Patterns.pdf | Published version | 2.16 MB |
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Mental health disorders may cause severe consequences for countries’ economies and health. Identifying early signs of these disorders is vital. The state-of-the-art research in identifying mental health disorder patterns from textual data, uses hand-labeled training sets, especially when a domain expert’s knowledge is required to analyze various symptoms in a patient. This task could be time-consuming and expensive. To address this challenge, in this paper, we study and analyze the various clinical and non-clinical approaches to identifying mental health disorders. We leverage the domain knowledge and expertise in cognitive science to build a domain-specific Knowledge Base for the mental health disorder concepts and patterns. We present a weaker form of supervision by facilitating and generating training data from a domain-specific Knowledge Base. We adopt a typical scenario for analyzing social media to identify depression symptoms from the textual content generated by social users.
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