Summary of Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (ema) Forecasting, by Mandani Ntekouli et al.
Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting
by Mandani Ntekouli, Gerasimos Spanakis, Lourens Waldorp, Anne Roefs
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper focuses on improving the analysis of ecological momentary assessment (EMA) data in psychopathology research. EMA collects contextually rich measurements over time, resulting in multivariate time series (MTS) data that poses challenges due to temporal complexities and interdependencies between variables. The study evaluates recurrent and temporal graph neural networks (GNNs) for MTS analysis, incorporating additional information from graphs reflecting variable relationships. Results show GNNs outperforming the baseline LSTM model with a mean squared error (MSE) of 0.84 compared to 1.02. The paper also explores the effect of constructing different graphs on GNN performance and finds that graph learning is promising for refining pre-defined graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps doctors and scientists better understand people’s emotions, behaviors, and situations by analyzing data collected over time. This type of data is complex because it involves many variables changing over time. The study uses special computer models called neural networks to analyze this data. It finds that these models can work well when they take into account the relationships between different variables. This could be important for predicting and understanding mental health issues. |
Keywords
» Artificial intelligence » Gnn » Lstm » Mse » Time series
