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

arXiv:2404.03678v1 Announce Type: new
Abstract: Diagnostic tests which 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 therefore 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. Here, we follow other recent proposals to further refine this concept, by using machine learning to …

abstract arxiv clinical control cs.lg diagnostic diseases human identify improving infection infectious diseases machine machine learning performance q-bio.pe stat.ap stat.ml test testing tests tools type weapons