Machine learning augmented diagnostic testing to identify sources of variability in test performance.

ABSTRACT: Diagnostic tests that can detect pre-clinical or sub-clinical infection, are one of the most powerful tools in our armoury of weapons to control infectious diseases. Considerable effort has been paid to improving diagnostic testing for human, plant and animal diseases, including strategies for targeting the use of diagnostic tests towards individuals who are more likely to be infected. We use machine learning to assess the surrounding risk landscape under which a diagnostic test is applied to augment its interpretation. We develop this to predict the occurrence of bovine tuberculosis incidents in cattle herds, exploiting the availability of exceptionally detailed testing records. We show that, without compromising test specificity, test sensitivity can be improved so that the proportion of

SUBMITTER: Banks CJ 

PROVIDER: S-EPMC12646444 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature