In this repository, you will find the code to our preprint titled "Rethinking the residual approach: Leveraging machine learning to operationalize cognitive resilience in Alzheimer’s disease". It has been submitted for publication in a peer-reviewed journal.
A pre-print will be published shortly.
src/: Contains all the source code.example.ipynb: An examplary run of the framework highlighting how the code can be used.expectation_elasitnec/: Elasitc net implementation that can be used as the expectation model.expectation_elasitnec/: xgboostimplementation that can be used as the expectation model.standard_approach/: A linear regression used for the standard approach and when modelling the expectation using a linear model.simulated_data/: All the scripts used for simulating the data and running the experiments presented in the manuscript.plotting.py: Functions used for creating the plots.utils.py: Utility functions used in the other scripts.
All dependencies are listed in the 'environment.yml' file and can be installed using conda.