Summary of Unscrambling Disease Progression at Scale: Fast Inference Of Event Permutations with Optimal Transport, by Peter A. Wijeratne et al.
Unscrambling disease progression at scale: fast inference of event permutations with optimal transport
by Peter A. Wijeratne, Daniel C. Alexander
First submitted to arxiv on: 18 Oct 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 paper presents a novel approach to modeling disease progression by leveraging optimal transport ideas. It infers group-level temporal trajectories of change in patients’ features as a chronic degenerative condition unfolds. This allows for unique insights into disease biology and staging systems with individual-level clinical utility. The model considers disease progression as a latent permutation of events, where each event corresponds to a feature becoming measurably abnormal. To facilitate fast inference, the paper uses optimisation of the variational lower bound, enabling a factor of 1000 times faster inference than the current state of the art. This is achieved by modeling disease progression as a latent permutation matrix of events belonging to the Birkhoff polytope. Experiments demonstrate the increase in speed, accuracy, and robustness to noise in simulation. The method is also applied to real-world imaging data from two separate datasets, one from Alzheimer’s disease patients and the other from age-related macular degeneration, showcasing pixel-level disease progression events in the brain and eye, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to understand how diseases like Alzheimer’s or age-related macular degeneration change over time. This helps doctors get a better picture of what’s happening inside patients’ bodies. The model looks at small changes that happen as these conditions progress and tries to make sense of them. It does this by treating the disease progression like a puzzle, where each piece is a small change in how someone’s body works. By using special math tricks, the model can solve this puzzle much faster than before. This means doctors could get important information about patients’ conditions much quicker. The method was tested with real data from people with Alzheimer’s and age-related macular degeneration and showed that it can provide valuable insights. |
Keywords
» Artificial intelligence » Inference
