Psychological disorder detection: A multimodal approach using a transformer-based hybrid model

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES MethodsX Pub Date : 2024-09-24 DOI:10.1016/j.mex.2024.102976
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Abstract

Detecting psychological disorders, particularly depression, is a complex and critical task within the realm of mental health assessment. This research explores a novel approach to improve the identification of psychological distresses, such as depression, by addressing the subjectivity, complexity, and biasness inherent in traditional diagnostic techniques. Using multimodal data, such as voice characteristics and linguistic content from participant interviews, we developed a Transformer-Based Hybrid Model that combines advanced natural language processing and deep learning approaches. This model provides a complete assessment of an individual's psychological well-being by merging aural cues and textual data. This study investigates the theoretical underpinnings, technical complexities, and practical applications of this model in the context of psychological disorder detection. Additionally, the model's design and implementation details are thoroughly documented to ensure replicability by other researchers.
  • A unique way of strengthening emotional ailments (focusing on depression).
  • Transformer-Based Hybrid Model is proposed using multimodal data from interviews of participants.
  • The model integrates voice characteristics (aural cues) and linguistic content (textual data).
  • Comparative analysis of this research with existing approaches.

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心理障碍检测:使用基于变压器的混合模型的多模式方法
检测心理障碍,尤其是抑郁症,是心理健康评估领域一项复杂而关键的任务。本研究探索了一种新方法,通过解决传统诊断技术中固有的主观性、复杂性和偏差,改进对抑郁症等心理困扰的识别。我们利用多模态数据,如来自参与者访谈的语音特征和语言内容,开发了一种基于变压器的混合模型,该模型结合了先进的自然语言处理和深度学习方法。该模型通过合并听觉线索和文本数据,提供了对个人心理健康的完整评估。本研究探讨了该模型的理论基础、技术复杂性以及在心理障碍检测中的实际应用。此外,还详细记录了该模型的设计和实施细节,以确保其他研究人员可以复制。-加强情绪疾病(重点是抑郁症)的独特方法-利用参与者访谈中的多模态数据提出基于变压器的混合模型-该模型整合了声音特征(听觉线索)和语言内容(文本数据)-该研究与现有方法的比较分析。
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来源期刊
MethodsX
MethodsX Health Professions-Medical Laboratory Technology
CiteScore
3.60
自引率
5.30%
发文量
314
审稿时长
7 weeks
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