Recommendations and future directions for supervised machine learning in psychiatry
Micah Cearns, Tim Hahn, Bernhard T Baune
Translational Psychiatry | NATURE PUBLISHING GROUP | Published : 2019
Machine learning methods hold promise for personalized care in psychiatry, demonstrating the potential to tailor treatment decisions and stratify patients into clinically meaningful taxonomies. Subsequently, publication counts applying machine learning methods have risen, with different data modalities, mathematically distinct models, and samples of varying size being used to train and test models with the promise of clinical translation. Consequently, and in part due to the preliminary nature of such works, many studies have reported largely varying degrees of accuracy, raising concerns over systematic overestimation and methodological inconsistencies. Furthermore, a lack of procedural eval..View full abstract
This work was supported by the Australian Government Research Training Program Scholarship.