Network-based characterization of latent co-testing patterns in antimicrobial susceptibility testing.

This research explores how network‑science tools can reveal hidden co‑testing patterns in antimicrobial susceptibility testing (AST) data. It treats clinical testing protocols as complex networks: each node is a specific antimicrobial drug, and each edge shows how often two drugs are tested together or how strongly their results correlate. By building these networks, the approach uncovers structural links that are not obvious in the raw workflow. The method creates bipartite and projected network models to study the shape of AST practices in many clinical settings. It uses measures such as degree centrality, modularity, and community‑detection algorithms to spot groups of drugs that are often prescribed or tested together. This network

view makes it easier to spot redundant tests, improve diagnostic panels, and find systematic biases in antimicrobial monitoring. To check the approach, the researchers run it on simulated or anonymized clinical datasets. They test whether the network metrics stay reliable when the amount of data (sampling density) changes. The ultimate aim is to give clinical decision‑support systems a clear structural guide that streamlines testing, cuts waste, and helps interpret multidrug‑resistance profiles through advanced network analysis.

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