Summary of Predicting Depression and Anxiety: a Multi-layer Perceptron For Analyzing the Mental Health Impact Of Covid-19, by David Fong and Tianshu Chu and Matthew Heflin and Xiaosi Gu and Oshani Seneviratne
Predicting Depression and Anxiety: A Multi-Layer Perceptron for Analyzing the Mental Health Impact of COVID-19
by David Fong, Tianshu Chu, Matthew Heflin, Xiaosi Gu, Oshani Seneviratne
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multi-layer perceptron (MLP) called CoDAP is introduced for predicting mental health trends during the COVID-19 pandemic. The model utilizes a comprehensive dataset tracking weekly mental health symptoms over ten weeks among U.S. adults from April to June 2020, a period marked by surging mental health symptoms and conditions. CoDAP not only predicts anxiety and depression patterns but also reveals key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health. This study contributes to a more nuanced understanding of complex mental health issues in global health crises, potentially informing future early interventions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We created a model called CoDAP to predict how people’s mental health changed during the COVID-19 pandemic. We used a big dataset that tracked people’s feelings and behaviors for 10 weeks. This was an important time because many people were feeling stressed and anxious. Our model looked at patterns in people’s behavior, like what they did and who they were, to see how it affected their mental health. We found some interesting things about how different people reacted differently to the pandemic. This helps us understand why some people might get depressed or anxious during a crisis, and maybe we can use this information to help people sooner. |
Keywords
* Artificial intelligence * Tracking
