Data-driven causal model discovery and personalized prediction in Alzheimer’s disease
07-06-2022
The following figure presents a flowchart of the proposed data-driven modeling approach. Given the initialized ODE model, a causal model is obtained by fitting the ADNI dataset and DPS model through sparse learning; secondly, the ADNI dataset is used to calibrate the population parameters in the causal model and obtain the population model; thirdly, a sensitivity analysis is applied to analyze the sensitivity of each population parameters and determine the sensitive personalized parameters, and a simulate study is conducted to validate the population model. Then, the personalized model is obtained by calibrating the sensitive personalized parameters with the use of personalized data. A prediction is made by the personalized model in the end.