Enhancing Depression Assessment with a Comprehensive Method for Heterogeneous Data

• Item type: SIG Technical Reports(1)
• 公開日: 2024-12-07
• タイトル: Enhancing Depression Assessment with a Comprehensive Method for Heterogeneous Data
• タイトル / 言語: en
• タイトル: Enhancing Depression Assessment with a Comprehensive Method for Heterogeneous Data
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Donghua University
• 著者所属(英): en
• 著者所属(英): Donghua University
• 著者名: Ruijia, Yan
• 著者名(英): Ruijia, Yan
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In recent years, mental health issues like depression have become a global concern, making accurate evaluation of depression severity essential for effective treatment. Traditional scales rely on single numeric values, which may not capture the uncertainty caused by the subjective and fluctuating nature of mental health symptoms, leading to inconsistencies. This paper introduces a novel method enabling clinicians to record symptoms using heterogeneous data, including quantitative (crisp numbers, intervals) and qualitative data (linguistic terms). Additionally, a linguistic scale algorithm is employed to model the linguistic term sets, converting each symptom into a cloud model characterized by three numeric features. Aggregating these models allows for a comprehensive evaluation of both depression severity and the associated uncertainty levels. This approach enhances diagnostic accuracy and reliability in mental health care.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In recent years, mental health issues like depression have become a global concern, making accurate evaluation of depression severity essential for effective treatment. Traditional scales rely on single numeric values, which may not capture the uncertainty caused by the subjective and fluctuating nature of mental health symptoms, leading to inconsistencies. This paper introduces a novel method enabling clinicians to record symptoms using heterogeneous data, including quantitative (crisp numbers, intervals) and qualitative data (linguistic terms). Additionally, a linguistic scale algorithm is employed to model the linguistic term sets, converting each symptom into a cloud model characterized by three numeric features. Aggregating these models allows for a comprehensive evaluation of both depression severity and the associated uncertainty levels. This approach enhances diagnostic accuracy and reliability in mental health care.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11253943
• 書誌情報: 研究報告情報システムと社会環境（IS）; 巻 2024-IS-170, 号 3, p. 1-5, 発行日 2024-12-07
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8809
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会