Depression Detection on Social Media with Large Language Models
- URL: http://arxiv.org/abs/2403.10750v1
- Date: Sat, 16 Mar 2024 01:01:16 GMT
- Title: Depression Detection on Social Media with Large Language Models
- Authors: Xiaochong Lan, Yiming Cheng, Li Sheng, Chen Gao, Yong Li,
- Abstract summary: Depression detection aims to determine whether an individual suffers from depression by analyzing their history of posts on social media.
We propose a novel depression detection system called DORIS, combining medical knowledge and the recent advances in large language models.
- Score: 23.075317886505193
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Depression harms. However, due to a lack of mental health awareness and fear of stigma, many patients do not actively seek diagnosis and treatment, leading to detrimental outcomes. Depression detection aims to determine whether an individual suffers from depression by analyzing their history of posts on social media, which can significantly aid in early detection and intervention. It mainly faces two key challenges: 1) it requires professional medical knowledge, and 2) it necessitates both high accuracy and explainability. To address it, we propose a novel depression detection system called DORIS, combining medical knowledge and the recent advances in large language models (LLMs). Specifically, to tackle the first challenge, we proposed an LLM-based solution to first annotate whether high-risk texts meet medical diagnostic criteria. Further, we retrieve texts with high emotional intensity and summarize critical information from the historical mood records of users, so-called mood courses. To tackle the second challenge, we combine LLM and traditional classifiers to integrate medical knowledge-guided features, for which the model can also explain its prediction results, achieving both high accuracy and explainability. Extensive experimental results on benchmarking datasets show that, compared to the current best baseline, our approach improves by 0.036 in AUPRC, which can be considered significant, demonstrating the effectiveness of our approach and its high value as an NLP application.
Related papers
- Depression Detection and Analysis using Large Language Models on Textual and Audio-Visual Modalities [25.305909441170993]
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.
arXiv Detail & Related papers (2024-07-08T17:00:51Z) - Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study [0.6524460254566905]
Depression has affected millions of people worldwide and has become one of the most common mental disorders.
Recent research has evidenced that machine learning (ML) and Natural Language Processing (NLP) tools and techniques have significantly been used to diagnose depression.
However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present.
arXiv Detail & Related papers (2024-04-03T19:45:40Z) - Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary
Task Integration [54.76511683427566]
This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information.
A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction.
The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
arXiv Detail & Related papers (2024-02-16T05:16:20Z) - 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) - DepreSym: A Depression Symptom Annotated Corpus and the Role of LLMs as
Assessors of Psychological Markers [3.5511184956329727]
We present the DepreSym dataset, consisting of 21580 sentences annotated according to their relevance to the Beck Depression Inventory-II symptoms.
This dataset serves as a valuable resource for advancing the development of models that incorporate depressive markers such as clinical symptoms.
arXiv Detail & Related papers (2023-08-21T14:44:31Z) - What Symptoms and How Long? An Interpretable AI Approach for Depression
Detection in Social Media [0.5156484100374058]
Depression is the most prevalent and serious mental illness, which induces grave financial and societal ramifications.
This study contributes to IS literature with a novel interpretable deep learning model for depression detection in social media.
arXiv Detail & Related papers (2023-05-18T20:15:04Z) - 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) - MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos.
We release a new dataset, MEDICA, for mental health patient engagement detection.
arXiv Detail & Related papers (2020-11-17T15:18:38Z) - 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.