Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory

• URL: http://arxiv.org/abs/2409.15084v2
• Date: Wed, 9 Oct 2024 04:37:29 GMT
• Title: Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory
• Authors: Kunyao Lan, Bingrui Jin, Zichen Zhu, Siyuan Chen, Shu Zhang, Kenny Q. Zhu, Mengyue Wu,
• Abstract summary: This paper introduces the Agent Mental Clinic (AMC), a self-improving conversational agent system designed to enhance depression diagnosis through simulated dialogues between patient and psychiatrist agents. We design a psychiatrist agent consisting of a tertiary memory structure, a dialogue control and a memory sampling module, fully leveraging the skills reflected by the psychiatrist agent, achieving great accuracy on depression risk and suicide risk diagnosis via conversation.
• Score: 35.41386783586689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mental health issues, particularly depressive disorders, present significant challenges in contemporary society, necessitating the development of effective automated diagnostic methods. This paper introduces the Agent Mental Clinic (AMC), a self-improving conversational agent system designed to enhance depression diagnosis through simulated dialogues between patient and psychiatrist agents. To enhance the dialogue quality and diagnosis accuracy, we design a psychiatrist agent consisting of a tertiary memory structure, a dialogue control and reflect plugin that acts as ``supervisor'' and a memory sampling module, fully leveraging the skills reflected by the psychiatrist agent, achieving great accuracy on depression risk and suicide risk diagnosis via conversation. Experiment results on datasets collected in real-life scenarios demonstrate that the system, simulating the procedure of training psychiatrists, can be a promising optimization method for aligning LLMs with real-life distribution in specific domains without modifying the weights of LLMs, even when only a few representative labeled cases are available.

Related papers

• MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
  Mental health disorders are one of the most serious diseases in the world. Privacy concerns limit the accessibility of personalized treatment data. MentalArena is a self-play framework to train language models.
  arXiv  Detail & Related papers  (2024-10-09T13:06:40Z)
• 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)
• Towards Reliable and Empathetic Depression-Diagnosis-Oriented Chats [15.36217265716081]
  We propose an innovative definition and generation framework tailored explicitly for depression diagnosis dialogues. The framework combines the reliability of task-oriented conversations with the appeal of empathy-related chit-chat. Exhaustive experimental results indicate significant improvements in task completion and emotional support generation in depression diagnosis.
  arXiv  Detail & Related papers  (2024-04-07T16:35:53Z)
• Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking [27.96718892323191]
  Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection. Recent work focuses on combining task-oriented dialogue and chitchat to simulate the interview-based depression diagnosis. No explicit framework has been explored to guide the dialogue, which results in some useless communications.
  arXiv  Detail & Related papers  (2024-03-12T07:17:01Z)
• Illuminate: A novel approach for depression detection with explainable analysis and proactive therapy using prompt engineering [0.0]
  This paper introduces a novel paradigm for depression detection and treatment using advanced Large Language Models (LLMs): Generative Pre-trained Transformer 4 (GPT-4), Llama 2 chat, and Gemini. LLMs are fine-tuned with specialized prompts to diagnose, explain, and suggest therapeutic interventions for depression.
  arXiv  Detail & Related papers  (2024-02-05T06:08:06Z)
• Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
  We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting. DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas. Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
  arXiv  Detail & Related papers  (2023-10-11T02:47:21Z)
• 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)
• D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat [25.852922703368133]
  In a depression-diagnosis-directed clinical session, doctors initiate a conversation with ample emotional support that guides the patients to expose their symptoms. Due to the social stigma associated with mental illness, the dialogue data related to depression consultation and diagnosis are rarely disclosed. We construct a Chinese dialogue dataset for Depression-Diagnosis-Oriented Chat which simulates the dialogue between doctors and patients during the diagnosis of depression.
  arXiv  Detail & Related papers  (2022-05-24T03:54:22Z)
• Hierarchical Reinforcement Learning for Automatic Disease Diagnosis [52.111516253474285]
  We propose to integrate a hierarchical policy structure of two levels into the dialogue systemfor policy learning. The proposed policy structure is capable to deal with diagnosis problem including large number of diseases and symptoms.
  arXiv  Detail & Related papers  (2020-04-29T15:02:41Z)

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.