基于文本和视听模态的大型语言模型抑郁检测与分析
Depression Detection and Analysis using Large Language Models on Textual and Audio-Visual Modalities
AI总结：
针对抑郁症诊断依赖医生经验易受偏见影响的问题，该研究利用大型语言模型处理文本和视听模态数据，在E-DAIC数据集上实现更优抑郁检测结果，还提出视听多模态网络提升PHQ-8分数预测性能。
摘要：
抑郁症已被证明是重大公共卫生问题，严重影响个体心理健康。若未被诊断，抑郁症会导致严重健康问题，表现为生理症状甚至自杀。通常，诊断抑郁症或其他精神障碍需进行半结构化访谈及补充问卷，包括临床医生和心理健康专业人员使用的患者健康问卷（PHQ）变体。该方法严重依赖训练有素医生的经验和判断，导致诊断易受个人偏见影响。由于抑郁症的潜在机制仍在积极研究中，医生在诊断和治疗时常面临挑战，尤其是在临床早期阶段。近年来，人工神经计算在解决文本、图像和语音等多领域问题上取得显著进展。我们的分析旨在利用这些最先进（SOTA）模型进行实验，通过多模态实现最优结果。实验在2019年视听情感挑战（AVEC）提出的扩展痛苦分析访谈语料库绿野仙踪数据集（E-DAIC）上进行。提出的方案显示专有和开源大型语言模型（LLMs）取得更好结果，在文本模态上实现3.98的均方根误差（RMSE），优于AVEC 2019挑战基线结果和当前SOTA回归分析架构；此外，该方案在分类任务中达到71.43%的准确率。论文还包含一种新颖的视听多模态网络，预测PHQ-8分数的RMSE为6.51。
英文摘要：
Depression has proven to be a significant public health issue, profoundly affecting the psychological well-being of individuals. If it remains undiagnosed, depression can lead to severe health issues, which can manifest physically and even lead to suicide. Generally, Diagnosing depression or any other mental disorder involves conducting semi-structured interviews alongside supplementary questionnaires, including variants of the Patient Health Questionnaire (PHQ) by Clinicians and mental health professionals. This approach places significant reliance on the experience and judgment of trained physicians, making the diagnosis susceptible to personal biases. Given that the underlying mechanisms causing depression are still being actively researched, physicians often face challenges in diagnosing and treating the condition, particularly in its early stages of clinical presentation. Recently, significant strides have been made in Artificial neural computing to solve problems involving text, image, and speech in various domains. Our analysis has aimed to leverage these state-of-the-art (SOTA) models in our experiments to achieve optimal outcomes leveraging multiple modalities. The experiments were performed on the Extended Distress Analysis Interview Corpus Wizard of Oz dataset (E-DAIC) corpus presented in the Audio/Visual Emotion Challenge (AVEC) 2019 Challenge. The proposed solutions demonstrate better results achieved by Proprietary and Open-source Large Language Models (LLMs), which achieved a Root Mean Square Error (RMSE) score of 3.98 on Textual Modality, beating the AVEC 2019 challenge baseline results and current SOTA regression analysis architectures. Additionally, the proposed solution achieved an accuracy of 71.43% in the classification task. The paper also includes a novel audio-visual multi-modal network that predicts PHQ-8 scores with an RMSE of 6.51.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
