Summary of Towards Modeling Evolving Longitudinal Health Trajectories with a Transformer-based Deep Learning Model, by Hans Moen et al.
Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model
by Hans Moen, Vishnu Raj, Andrius Vabalas, Markus Perola, Samuel Kaski, Andrea Ganna, Pekka Marttinen
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Transformer-based deep learning model is designed to analyze individuals’ health trajectories over time by predicting the onset of common diseases. The approach modifies the training objective and applies a causal attention mask to allow for continuous predictions at every time point until a given forecast period. The model performs comparably to other models in terms of basic prediction performance while offering promising trajectory modeling properties. Applications include analyzing health trajectories, aiding early detection of events that forecast possible later disease onsets, and enabling interventions in ongoing health trajectories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how people’s health changes over time using a special kind of artificial intelligence model called a Transformer. The goal is to predict when common diseases might happen in the future. Instead of just giving one prediction, this model can make predictions at every point until a certain date. It’s like having a health tracker that can warn you if something might go wrong. The results show that this model works well and could be useful for tracking people’s health and helping doctors catch problems early. |
Keywords
» Artificial intelligence » Attention » Deep learning » Mask » Tracking » Transformer
