HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection

• URL: http://arxiv.org/abs/2408.03648v1
• Date: Wed, 7 Aug 2024 09:23:01 GMT
• Title: HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection
• Authors: Juho Jung, Chaewon Kang, Jeewoo Yoon, Seungbae Kim, Jinyoung Han,
• Abstract summary: HiQuE is a novel depression detection framework that leverages the hierarchical relationship between primary and follow-up questions in clinical interviews. We conduct extensive experiments on the widely-used clinical interview data, DAIC-WOZ, where our model outperforms other state-of-the-art multimodal depression detection models.
• Score: 11.984035389013426
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The utilization of automated depression detection significantly enhances early intervention for individuals experiencing depression. Despite numerous proposals on automated depression detection using recorded clinical interview videos, limited attention has been paid to considering the hierarchical structure of the interview questions. In clinical interviews for diagnosing depression, clinicians use a structured questionnaire that includes routine baseline questions and follow-up questions to assess the interviewee's condition. This paper introduces HiQuE (Hierarchical Question Embedding network), a novel depression detection framework that leverages the hierarchical relationship between primary and follow-up questions in clinical interviews. HiQuE can effectively capture the importance of each question in diagnosing depression by learning mutual information across multiple modalities. We conduct extensive experiments on the widely-used clinical interview data, DAIC-WOZ, where our model outperforms other state-of-the-art multimodal depression detection models and emotion recognition models, showcasing its clinical utility in depression detection.

Related papers

• Multi-aspect Depression Severity Assessment via Inductive Dialogue System [5.156059061769101]
  We present a novel task of multi-aspect depression severity assessment via an inductive dialogue system (MaDSA) We propose a foundational system for MaDSA, which induces psychological dialogue responses with an auxiliary emotion classification task. We synthesize the conversational dataset annotated with eight aspects of depression severity alongside emotion labels, proven robust via human evaluations.
  arXiv  Detail & Related papers  (2024-10-29T08:00:08Z)
• CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy [67.23830698947637]
  We propose a new benchmark, CBT-BENCH, for the systematic evaluation of cognitive behavioral therapy (CBT) assistance. We include three levels of tasks in CBT-BENCH: I: Basic CBT knowledge acquisition, with the task of multiple-choice questions; II: Cognitive model understanding, with the tasks of cognitive distortion classification, primary core belief classification, and fine-grained core belief classification; III: Therapeutic response generation, with the task of generating responses to patient speech in CBT therapy sessions. Experimental results indicate that while LLMs perform well in reciting CBT knowledge, they fall short in complex real-world scenarios
  arXiv  Detail & Related papers  (2024-10-17T04:52:57Z)
• LLM Questionnaire Completion for Automatic Psychiatric Assessment [49.1574468325115]
  We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C)
  arXiv  Detail & Related papers  (2024-06-09T09:03:11Z)
• Speech-based Clinical Depression Screening: An Empirical Study [32.84863235794086]
  This study investigates the utility of speech signals for AI-based depression screening across varied interaction scenarios. participants include depressed patients recruited from the outpatient clinics of Peking University Sixth Hospital. We extracted acoustic and deep speech features from each participant's segmented recordings.
  arXiv  Detail & Related papers  (2024-06-05T09:43:54Z)
• DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic Depression Detection from Clinical Interviews [39.08557916089242]
  Recent studies have reported enhanced performance when incorporating interviewer's prompts into the model. We discover that models using interviewer's prompts learn to focus on a specific region of the interviews, where questions about past experiences with mental health issues are asked. We achieve a 0.90 F1 score by intentionally exploiting it, the highest result reported to date on this dataset using only textual information.
  arXiv  Detail & Related papers  (2024-04-22T09:07:50Z)
• Towards Mitigating Hallucination in Large Language Models via Self-Reflection [63.2543947174318]
  Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks. This paper analyses the phenomenon of hallucination in medical generative QA systems using widely adopted LLMs and datasets.
  arXiv  Detail & Related papers  (2023-10-10T03:05:44Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data [50.02223091927777]
  Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
  arXiv  Detail & Related papers  (2020-12-05T05:14:14Z)
• Where's the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data [83.89578557287658]
  We propose a novel multi-channel deep convolutional neural network architecture, namely Quest-CNN, for the purpose of separating real questions. We conducted a comprehensive performance comparison analysis of the proposed network against other deep neural networks. The proposed Quest-CNN achieved the best F1 score both on a dataset of data entry-review dialogue in a dialysis care setting, and on a general domain dataset.
  arXiv  Detail & Related papers  (2020-10-15T15:11:22Z)
• Multimodal Depression Severity Prediction from medical bio-markers using Machine Learning Tools and Technologies [0.0]
  Depression has been a leading cause of mental-health illnesses across the world. Using behavioural cues to automate depression diagnosis and stage prediction in recent years has relatively increased. The absence of labelled behavioural datasets and a vast amount of possible variations prove to be a major challenge in accomplishing the task.
  arXiv  Detail & Related papers  (2020-09-11T20:44:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.