Leveraging Regression Transformers and SHAP for Alzheimer’s Research: A Path to Deeper Insights

Music City Center 

We leverage machine learning models to better understand Alzheimer's Disease by using cognitive scores as the dependent variable and other clinical variables as explanatory variables to uncover their influence on the disease's development. Specifically, we employ the Regression Transformer model, a specialized adaptation of the transformer architecture tailored for regression analysis and its self-attention mechanism to model dependencies across both temporal and feature dimensions as well as missing data. In addition, we couple with SHAP (Shapley Additive exPlanations) to tackle the interpretability issue with machine learning models. We evaluate our approach with previous work in machine learning on Alzheimer's disease progression such as the reinforcement learning as well as the traditional mixed-effects regression through simulation and by applying to the A4 trials data to gain additional insights.

Machine learning

Regression Transformer

interpretability

SHAP (Shapley Additive exPlanations)

Alzheimer’s Disease

Missing Data 

International Chinese Statistical Association