Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning.

ABSTRACT:

Importance

The diagnosis of schizophrenia and bipolar disorder is often delayed several years despite illness typically emerging in late adolescence or early adulthood, which impedes initiation of targeted treatment.

Objective

To investigate whether machine learning models trained on routine clinical data from electronic health records (EHRs) can predict diagnostic progression to schizophrenia or bipolar disorder among patients undergoing treatment in psychiatric services for other mental illness.

Design, setting, and participants

This cohort study was based on data from EHRs from the Psychiatric Services of the Central Denmark Region. All patients aged 15 to 60 years with at least 2 contacts (at least 3 months apart) with the Psychiatric Services of the Central Denma

SUBMITTER: Hansen L 

PROVIDER: S-EPMC12551142 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature