We want to support general contrastive learning window definitions.
There are some common patient specific latent space temporal structures we may desire (as shown in Figure A).
[ ] Consistent - that a patient has a constant representation if we look at two different windows of time for them. This can be local (only for adjacent windows) or global (for any two randomly selected non-overlapping windows)
[ ] Continuity/interpolation - that if we take two non adjacent windows for the patient, the window between them should be approximately an average of those two windows.
[ ] Ordering - that if we reverse time, or shuffle windows and input those to a model, the representation will be very different. This can also be local (adjacent windows) or global (no adjacency constraint).
We can also have multi-patient properties:
[ ] Label-Based Neighbors - patients with the same label should have similar representations
Additionally, we want to allow users to select global and local windows a
We want to support general contrastive learning window definitions.
There are some common patient specific latent space temporal structures we may desire (as shown in Figure A).
Additionally, we want to allow users to select global and local windows a