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

David Fong, Tianshu Chu, Matthew Heflin, Xiaosi Gu, Oshani Seneviratne
3/9/2024
cs.LGcs.CY

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Exploring Social Media for Early Detection of Depression in COVID-19 Patients

85% match1 repo42023

Identifying Risk Factors for Post-COVID-19 Mental Health Disorders: A Machine Learning Perspective

85% match2023

On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook

83% match1 repo752024

Exploration of Adolescent Depression Risk Prediction Based on Census Surveys and General Life Issues

81% match2024

Large-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants

81% match2024

The RW3D: A multi-modal panel dataset to understand the psychological impact of the pandemic

81% match2023

Cite this paper

@article{fong2024predicting,
  title  = {Predicting Depression and Anxiety: A Multi-Layer Perceptron for Analyzing the Mental Health Impact of COVID-19},
  author = {David Fong and Tianshu Chu and Matthew Heflin and Xiaosi Gu and Oshani Seneviratne},
  year   = {2024},
  eprint = {2403.06033},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.06033v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2403.06033.svg)](https://codeofpaper.com/paper/2403.06033)

Discussion