title: Integrating Large Language Models into a Tri-Modal Architecture for
    Automated Depression Classification
summary: Major Depressive Disorder (MDD) is a pervasive mental health condition that
  affects 300 million people worldwide. This work presents a novel, BiLSTM-based
  tri-modal model-level fusion architecture for the binary classification of
  depression from clinical interview recordings. The proposed architecture
  incorporates Mel Frequency Cepstral Coefficients, Facial Action Units, and uses
  a two-shot learning based GPT-4 model to process text data. This is the first
  work to incorporate large language models into a multi-modal architecture for
  this task. It achieves impressive results on the DAIC-WOZ AVEC 2016 Challenge
  cross-validation split and Leave-One-Subject-Out cross-validation split,
  surpassing all baseline models and multiple state-of-the-art models. In
  Leave-One-Subject-Out testing, it achieves an accuracy of 91.01%, an F1-Score
  of 85.95%, a precision of 80%, and a recall of 92.86%.
paper_authors._id: 66a837833873fba1999297fc
paper_authors.name: Santosh V. Patapati
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-30T07:26:04.036Z
paper_authors._id: 64ba972e90dfdda6ab7fbf54
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64ba972e90dfdda6ab7fbf54/631OMPITxvxH6u4sGhoHa.jpeg
paper_authors.fullname: Santosh Patapati
paper_authors.type: user
paper_authors.user: Soontosh
paper_title: Integrating Large Language Models into a Tri-Modal Architecture for
    Automated Depression Classification
paper_summary: Major Depressive Disorder (MDD) is a pervasive mental health condition that
  affects 300 million people worldwide. This work presents a novel, BiLSTM-based
  tri-modal model-level fusion architecture for the binary classification of
  depression from clinical interview recordings. The proposed architecture
  incorporates Mel Frequency Cepstral Coefficients, Facial Action Units, and uses
  a two-shot learning based GPT-4 model to process text data. This is the first
  work to incorporate large language models into a multi-modal architecture for
  this task. It achieves impressive results on the DAIC-WOZ AVEC 2016 Challenge
  cross-validation split and Leave-One-Subject-Out cross-validation split,
  surpassing all baseline models and multiple state-of-the-art models. In
  Leave-One-Subject-Out testing, it achieves an accuracy of 91.01%, an F1-Score
  of 85.95%, a precision of 80%, and a recall of 92.86%.
paper_ai_summary: A BiLSTM-based multi-modal model combines audio, facial, and text data using a GPT-4 model to classify depression, achieving state-of-the-art performance in binary classification tasks.
paper_ai_keywords: BiLSTM Mel Frequency Cepstral Coefficients Facial Action Units GPT-4 two-shot learning multi-modal architecture