Summary of Probabilistic Temporal Prediction Of Continuous Disease Trajectories and Treatment Effects Using Neural Sdes, by Joshua Durso-finley et al.
Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs
by Joshua Durso-Finley, Berardino Barile, Jean-Pierre Falet, Douglas L. Arnold, Nick Pawlowski, Tal Arbel
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV); Quantitative Methods (q-bio.QM)
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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 stochastic causal temporal framework for modeling disease progression via Neural Stochastic Differential Equations (NSDE). The proposed model takes MRI and tabular data as input, predicts factual and counterfactual progression trajectories on different treatments in latent space, and estimates high-confidence personalized treatment effects. The framework is applied to a large dataset of patient 3D MRI and clinical data for multiple sclerosis (MS) treatment trials, achieving accurate predictions of future MS disability evolution and treatment effects. Additionally, the model identifies subgroups of patients with high confidence in their response to treatment even when clinical endpoints are not reached. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a special kind of X-ray that can show how well your body is doing. This paper uses this technology to help doctors predict what might happen to people who have multiple sclerosis (MS). MS is a disease that affects the brain and spinal cord, and it’s very hard to treat. The goal is to find ways to make treatment more effective by understanding how each person’s body will respond. The researchers used special computer algorithms called Neural Stochastic Differential Equations (NSDE) to analyze MRI scans and other data from patients with MS. They were able to predict what would happen if different treatments were given, which could help doctors choose the best course of treatment for each patient. |
Keywords
» Artificial intelligence » Latent space
