Revealing the impact of psychiatric comorbidities on treatment outcome in early psychosis using counterfactual model explanation.

ABSTRACT:

Introduction

Psychiatric comorbidities have a significant impact on the course of illness in patients with schizophrenia spectrum disorders. To accurately predict outcomes for individual patients using computerized prognostic models, it is essential to consider these comorbidities and their influence.

Methods

In our study, we utilized a multi-modal deep learning architecture to forecast symptomatic remission, focusing on a multicenter sample of patients with first-episode psychosis from the OPTiMiSE study. Additionally, we introduced a counterfactual model explanation technique to examine how scores on the Mini International Neuropsychiatric Interview (MINI) affected the likelihood of remission, both at the group level and for individual patients.

Results

Our findings

SUBMITTER: van Dee V 

PROVIDER: S-EPMC10602778 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature