I have read multiple articles that have used machine learning algorithms (convolutional neural network, random forest, support vector regression, and gaussian process regression) on cross-sectional MRI data. I am wondering whether it is possible to apply these same methods to longitudinal or clustered data with repeated measures? If so, is there an algorithm that might be better to use?
I would be interested in seeing how adding longitudinal data could improve the performance of these types of machine learning models. So far, I am only aware of using mixed effect-models or generalized estimating equation on longitudinal data, but I am reading books and papers to learn more. Any advice or resources would be greatly appreciated.
Hello Robert, there are extensions of recursive partitioning and trees for longitudinal and clustered data. They essentially include a mixed model element into the algorithm. I have used the RE-EM algorithm in the past (see DOI: 10.1007/s10994-011-5258-3 and DOI: 10.1016/j.csda.2015.02.004). There are also binary partitioning for continuous longitudinal data (DOI: 10.1002/sim.1266) and mixed-effect random forest (DOI: 10.1080/00949655.2012.741599). Implementations can be found in R packages: REEMtree, longRPart2, MixRF.
Hello Robert,
I would suggest matrix factorization and in particular Bayesian matrix/tensor factorization techniques. I successfully applied these methods to model EDSS trajectories during my master thesis. You can use different Python packages to fit these models, i.e surprise, smurff.
Best,
Eliana
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