Towards Knowledge-based Mining of Mental Disorder Patterns from Textual Data

• URL: http://arxiv.org/abs/2207.06254v1
• Date: Thu, 7 Jul 2022 10:04:43 GMT
• Title: Towards Knowledge-based Mining of Mental Disorder Patterns from Textual Data
• Authors: Maryam Shahabikargar
• Abstract summary: Mental health disorders may cause severe consequences on all the countries' economies and health. Identifying early signs of mental health disorders is vital. For example, depression may increase an individual's risk of suicide.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mental health disorders may cause severe consequences on all the countries' economies and health. For example, the impacts of the COVID-19 pandemic, such as isolation and travel ban, can make us feel depressed. Identifying early signs of mental health disorders is vital. For example, depression may increase an individual's risk of suicide. The state-of-the-art research in identifying mental disorder patterns from textual data, uses hand-labelled training sets, especially when a domain expert's knowledge is required to analyse various symptoms. This task could be time-consuming and expensive. To address this challenge, in this paper, we study and analyse the various clinical and non-clinical approaches to identifying mental health disorders. We leverage the domain knowledge and expertise in cognitive science to build a domain-specific Knowledge Base (KB) for the mental health disorder concepts and patterns. We present a weaker form of supervision by facilitating the generating of training data from a domain-specific Knowledge Base (KB). We adopt a typical scenario for analysing social media to identify major depressive disorder symptoms from the textual content generated by social users. We use this scenario to evaluate how our knowledge-based approach significantly improves the quality of results.

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)
• 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)
• An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts [0.0]
  We construct and release a new annotated dataset with human-labelled explanations and classification of Interpersonal Risk Factors (IRF) affecting mental disturbance on social media. We establish baseline models on our dataset facilitating future research directions to develop real-time personalized AI models by detecting patterns of TBe and PBu in emotional spectrum of user's historical social media profile.
  arXiv  Detail & Related papers  (2023-05-30T04:08:40Z)
• UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts [0.0]
  We propose UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensembling of BERTs for producing reliable and well-calibrated predictions. We analyze unstructured user data on Reddit to classify six possible types of mental illnesses- None, Depression, Anxiety, Bipolar Disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2023-04-10T12:18:53Z)
• Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach [17.07576768682415]
  Depression is a common disease worldwide. It is difficult to diagnose and continues to be underdiagnosed. Because depressed patients constantly share their symptoms, major life events, and treatments on social media, researchers are turning to user-generated digital traces on social media for depression detection. We propose a Deep Knowledge-aware Depression Detection (DKDD) framework to accurately detect social media users at risk of depression and explain the critical factors that contribute to such detection.
  arXiv  Detail & Related papers  (2023-03-06T20:08:07Z)
• Predicting mental health using social media: A roadmap for future development [0.0]
  Mental disorders such as depression and suicidal ideation affect more than 300 million people over the world. On social media, mental disorder symptoms can be observed, and automated approaches are increasingly capable of detecting them. This research offers a roadmap for analysis, where mental state detection can be based on machine learning techniques.
  arXiv  Detail & Related papers  (2023-01-25T08:08:29Z)
• Semantic Similarity Models for Depression Severity Estimation [53.72188878602294]
  This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We use test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. We evaluate our methods on two Reddit-based benchmarks, achieving 30% improvement over state of the art in terms of measuring depression severity.
  arXiv  Detail & Related papers  (2022-11-14T18:47:26Z)
• Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [55.653944436488786]
  According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD. This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2022-07-03T11:33:52Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• Data set creation and empirical analysis for detecting signs of depression from social media postings [0.0]
  Depression is a common mental illness that has to be detected and treated at an early stage to avoid serious consequences. We developed a gold standard data set that detects the levels of depression as not depressed', moderately depressed' and severely depressed' from the social media postings.
  arXiv  Detail & Related papers  (2022-02-07T10:24:33Z)
• 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)

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.