Mixed Effects Random Forests for Personalised Predictions of Clinical Depression Severity
This addresses personalized prediction of depression severity for patients, but it is incremental as it builds on existing mixed effects and random forest methods.
This work tackled predicting clinical depression severity using multimodal data from 31 patients, showing that mixed effects random forests outperform standard random forests and personal average baselines by reducing mean absolute error by 0.199-0.276 on average per patient.
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.