Ghosh Debadrita, Karande Hema, Gite Shilpa, Pradhan Biswajeet
Artificial Intelligence & Machine Learning Department, Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India.
Symbiosis Centre of Applied AI (SCAAI), Symbiosis International (Deemed) University, Pune 412115, India.
MethodsX. 2024 Sep 24;13:102976. doi: 10.1016/j.mex.2024.102976. eCollection 2024 Dec.
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.
在心理健康评估领域，检测心理障碍，尤其是抑郁症，是一项复杂且关键的任务。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、复杂性和偏差性，来改进对诸如抑郁症等心理困扰的识别。利用多模态数据，如参与者访谈中的语音特征和语言内容，我们开发了一种基于Transformer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。通过融合听觉线索和文本数据，该模型对个体的心理健康状况进行全面评估。本研究探讨了该模型在心理障碍检测背景下的理论基础、技术复杂性和实际应用。此外，该模型的设计和实现细节被详细记录，以确保其他研究人员能够进行复制。
•一种强化情绪疾病（重点关注抑郁症）的独特方法。
•利用参与者访谈的多模态数据提出了基于Transformer的混合模型。
•该模型整合了语音特征（听觉线索）和语言内容（文本数据）。
•本研究与现有方法的对比分析。
