Opaque Ontology: Neuroimaging Classification of ICD-10 Diagnostic Groups in the UK Biobank.

ABSTRACT:

Background

1.The use of machine learning to classify diagnostic cases versus controls defined based on diagnostic ontologies such as the 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

2.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 trained using either neuroimaging (structural or funct

SUBMITTER: Easley T 

PROVIDER: S-EPMC11042365 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature