Predicting Depression and Anxiety: A Multi-Layer Perceptron for Analyzing the Mental Health Impact of COVID-19

• URL: http://arxiv.org/abs/2403.06033v1
• Date: Sat, 9 Mar 2024 22:49:04 GMT
• Title: Predicting Depression and Anxiety: A Multi-Layer Perceptron for Analyzing the Mental Health Impact of COVID-19
• Authors: David Fong and Tianshu Chu and Matthew Heflin and Xiaosi Gu and Oshani Seneviratne
• Abstract summary: We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends during the COVID-19 pandemic. Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults. This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health.
• Score: 1.9809980686152868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, during the COVID-19 pandemic. Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults. This period, characterized by a surge in mental health symptoms and conditions, offers a critical context for our analysis. Our focus was to extract and analyze patterns of anxiety and depression through a unique lens of qualitative individual attributes using CoDAP. This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health. These findings contribute to a more nuanced understanding of the complexity of mental health issues in times of global health crises, potentially guiding future early interventions.

Related papers

• On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook [21.978924582262263]
  Depression is the most widely studied mental health condition. The COVID-19 global pandemic has had a great impact on mental health worldwide. We present a survey on natural language processing (NLP) approaches to modeling depression in social media.
  arXiv  Detail & Related papers  (2024-10-11T13:20:54Z)
• 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)
• Large-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants [2.2909783327197393]
  We conducted a cross-sectional analysis of data from 10,129 participants recruited from a UK-based general population. Participants shared wearable (Fitbit) data and self-reported questionnaires on depression (PHQ-8), anxiety (GAD-7), and mood via a study app. We observed significant associations between the severity of depression and anxiety with several factors, including mood, age, gender, BMI, sleep patterns, physical activity, and heart rate.
  arXiv  Detail & Related papers  (2024-09-24T16:05:17Z)
• 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)
• Exploring Social Media for Early Detection of Depression in COVID-19 Patients [44.76299288962596]
  Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. We managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression.
  arXiv  Detail & Related papers  (2023-02-23T14:13:52Z)
• Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
  Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
  arXiv  Detail & Related papers  (2023-02-05T22:33:49Z)
• Capturing social media expressions during the COVID-19 pandemic in Argentina and forecasting mental health and emotions [0.802904964931021]
  We forecast mental health conditions and emotions of a given population during the COVID-19 pandemic in Argentina based on language expressions used in social media. Mental health conditions and emotions are captured via markers, which link social media contents with lexicons.
  arXiv  Detail & Related papers  (2021-01-12T15:15:31Z)
• 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)
• Anxiety Detection Leveraging Mobile Passive Sensing [53.11661460916551]
  Anxiety disorders are the most common class of psychiatric problems affecting both children and adults. Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods. eWellness is an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner.
  arXiv  Detail & Related papers  (2020-08-09T20:22:52Z)
• Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia [17.856486813652932]
  This paper studies community depression dynamics due to COVID-19 pandemic through user-generated content on Twitter. We study the problem using recently scraped tweets from Twitter users emanating from the state of New South Wales in Australia. Our novel classification model is capable of extracting depression polarities which may be affected by COVID-19.
  arXiv  Detail & Related papers  (2020-07-05T12:55:34Z)

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.