Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.

ABSTRACT:

Objective

Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features.

Methods

Late-life depression patients (medicated post-recruitment) (n = 33) and older non-depressed individuals (n = 35) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pretreatment. Linear and nonlinear

SUBMITTER: Patel MJ 

PROVIDER: S-EPMC4683603 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature