  This work demonstrates how mixed effects random forests enable accurate
predictions of depression severity using multimodal physiological and digital
activity data collected from an 8-week study involving 31 patients with major
depressive disorder. We show that mixed effects random forests outperform
standard random forests and personal average baselines when predicting clinical
Hamilton Depression Rating Scale scores (HDRS_17). Compared to the latter
baseline, accuracy is significantly improved for each patient by an average of
0.199-0.276 in terms of mean absolute error (p<0.05). This is noteworthy as
these simple baselines frequently outperform machine learning methods in mental
health prediction tasks. We suggest that this improved performance results from
the ability of the mixed effects random forest to personalise model parameters
to individuals in the dataset. However, we find that these improvements pertain
exclusively to scenarios where labelled patient data are available to the model
at training time. Investigating methods that improve accuracy when generalising
to new patients is left as important future work.
