A new open-source project called Biological JEJA combines JEPA-style representation learning with biological constraints to model how diseases progress over time. The approach was tested on real ADNI data from 2,347 patients and compared against several other models.

  • The method focuses on learning representations that capture disease changes over time rather than just predicting a patient's current state.
  • It utilizes real-world data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
  • The project includes patient-level inference and an evaluation framework that compares results with LLM heads.

The author is seeking feedback on the biological constraints, experimental setup, and the convincing nature of the results.