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- W4319586240 abstract "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." @default.
- W4319586240 created "2023-02-09" @default.
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- W4319586240 date "2022-10-13" @default.
- W4319586240 modified "2023-09-24" @default.
- W4319586240 title "Domain Knowledge Enhanced Text Mining for Identifying Mental Disorder Patterns" @default.
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- W4319586240 doi "https://doi.org/10.1109/dsaa54385.2022.10032346" @default.
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