Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets.

ABSTRACT: Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via "trial and error". Given the varied presentation of MDD and heterogeneity of treatment response, the use of machine learning to understand complex, non-linear relationships in data may be key for treatment personalization. Well-organized, structured data from clinical trials with standardized outcome measures is useful for training machine learning models; however, combining data across trials poses numerous challenges. There is also persistent concern that machine learning models can propagate harmful biases. We have created a methodology for organizing and preprocessing depression clinical trial data such that transformed variables harmonized across disparate datasets

SUBMITTER: Perlman K 

PROVIDER: S-EPMC11192904 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature