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
- 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) - 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) - Identifying Risk Factors for Post-COVID-19 Mental Health Disorders: A
Machine Learning Perspective [0.0]
We leveraged machine learning techniques to identify risk factors associated with post-COVID-19 mental health disorders.
Age, gender, and geographical region of residence were significant demographic factors influencing the likelihood of developing mental health disorders.
Comorbidities and the severity of COVID-19 illness were important clinical predictors.
arXiv Detail & Related papers (2023-09-27T22:30:11Z) - 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.