Fig. 2.

An overview schematic of the model design. The colour coding is as follows: blue for user input; pink for output of the method; and white for intermediary output and processes. First, equivalence filters use the bacteria resistance records from pre-processed data with optional bacteria and antimicrobial features, and produce equivalence classes [bacteria/antimicrobial pairs (BAPs)] based on the features. Then, differential analysis takes (i) the equivalence classes and (ii) a scope parameter that designates the number of periods of time that should be covered by the differential analysis for each BAP. Differential analysis will produce a set of bacterial/antimicrobial resistance distribution tuples (BARDs) that are used through structural analysis to produce the resistance difference maps. Graph analysis takes those maps with a similarity threshold and detects similar behaviours across BAPs. Meanwhile, the differences between BARDs are quantified to the nearest 2% difference interval, producing two sets: (i) a set of observations of interval differences; and (ii) a set of sequences of observations that form a training base for the hidden Markov model (HMM). Both sets are fed to the HMM training process to produce the HMM. The sequence generation process takes a short-term resistance difference query from the user, adds it as a suffix to existing sequences, and generates prediction evaluation sequences used for future predication. The prediction evaluation sequences can also be fed with a threshold to the HMM evaluation process to produce bacteria/antimicrobial short-term therapeutic value predictions. (For interpretation of reference to colour in this figure legend, the reader is referred to the web version of this article.)