Using machine learning to forecast symptom changes among subclinical depression patients receiving stepped care or usual care.

ABSTRACT:

Background

Subclinical depression (SD) is a mental health disorder characterized by minor depressive symptoms. Most SD patients are treated in the primary practice, but many respond poorly to treatment at the expense of provider resources. Stepped care approaches are appealing for tiering SD care to efficiently allocate scarce resources while jointly optimizing patient outcomes. However, stepped care can be time inefficient, as some persons may respond poorly and be forced to suffer with their symptoms for prolonged periods. Machine learning can offer insight into optimal treatment paths and inform clinical recommendations for incident patients.

Methods

As part of the Step-Dep trial, participants with SD were randomized to receive stepped care (N=96) or usual care (N=140). M

SUBMITTER: Scodari BT 

PROVIDER: S-EPMC10548339 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature