Using a machine learning approach to investigate factors associated with treatment-resistant depression among adults with chronic non-cancer pain conditions and major depressive disorder.

ABSTRACT:

Objective

Presence of chronic non-cancer pain conditions (CNPC) among adults with major depressive disorder (MDD) may reduce benefits of antidepressant therapy, thereby increasing the possibility of treatment resistance. This study sought to investigate factors associated with treatment-resistant depression (TRD) among adults with MDD and CNPC using machine learning approaches.

Methods

This retrospective cohort study was conducted using a US claims database which included adults with newly diagnosed MDD and CNPC (January 2007-June 2017). TRD was identified using a clinical staging algorithm for claims data. Random forest (RF), a machine learning method, and logistic regression was used to identify factors associated with TRD. Initial model development included 42 known and/o

SUBMITTER: Shah D 

PROVIDER: S-EPMC8393457 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature