Opaque ontology: neuroimaging classification of ICD-10 diagnostic groups in the UK Biobank.

ABSTRACT:

Background

The use of machine learning to classify diagnostic cases versus controls defined based on diagnostic ontologies such as the International Classification of Diseases, Tenth Revision (ICD-10) from neuroimaging features is now commonplace across a wide range of diagnostic fields. However, transdiagnostic comparisons of such classifications are lacking. Such transdiagnostic comparisons are important to establish the specificity of classification models, set benchmarks, and assess the value of diagnostic ontologies.

Results

We investigated case-control classification accuracy in 17 different ICD-10 diagnostic groups from Chapter V (mental and behavioral disorders) and Chapter VI (diseases of the nervous system) using data from the UK Biobank. Classification models were

SUBMITTER: Easley T 

PROVIDER: S-EPMC11811528 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature