The present application relates to the technical field of computer
information processing, and particularly relates to a
klebsiella pneumoniae
drug resistance detection method,
system, device and medium, the method comprising: acquiring and
processing original
mass spectrum data of a to-be-tested strain, determining topological persistence to screen characteristic peaks, and constructing an ordered
characteristic sequence; generating a view pair using data enhancement, and mapping it into a position encoding and intensity embedding
tensor, combining embedding labels to construct an input sequence; inputting the input sequence into an interpretable contrastive
learning network, using an attention mechanism and contrastive learning to optimize representation consistency and update a feature extractor; finally, freezing the extractor parameters and connecting a classification head, adjusting using labeled samples to realize discrimination of collaborative representation. Thus, by combining topological persistence
feature extraction and an interpretable contrastive
learning network, using an attention mechanism and aggregation logic, the present application eliminates the
black box property in the decision-making process, and realizes high-precision
drug resistance detection with clinical reliability.