Summary of Adopting Trustworthy Ai For Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations, by Pegah Ahadian et al.
Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations
by Pegah Ahadian, Wei Xu, Sherry Wang, Qiang Guan
First submitted to arxiv on: 25 Dec 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 The proposed research tackles the crucial issue of sleep disorder prediction by developing three deep time series models: Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM), and Temporal Fusion Transformer model (TFT). These models are equipped with explainability approaches, including temporal attention mechanism and SHapley Additive exPlanations (SHAP) for dependable and accurate predictions. The method is evaluated on a large dataset of sleep health measures, demonstrating its effectiveness in predicting sleep disorders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a way to predict sleep disorders using lifestyle and physiological data. They used three different models: TCN, LSTM, and TFT. These models help explain why the predictions were made. The method was tested on a big dataset of sleep health information and showed that it can accurately predict sleep disorders. |
Keywords
» Artificial intelligence » Attention » Lstm » Time series » Transformer
