Conclusions
Microbiologists are conditioned to approach a scientific subject with a hypothesis, a protocol outlining how to proceed, and a clear idea of what they are looking for. Data mining is a relatively new concept to microbiologists, and requires a change in mind set,as a key element of this approach is to apply methods developed for other fields like statistics and bioinformatics to a search for novel facts within the accumulated data.The papers cited in Section 2 of this chapter are included here to encourage workers interested in the subject of antimicrobial resistance to approach their subject within a new paradigm. The three general methods presented in some detail, antibiotypes, multivariate analysis, and evolutionary genetics,are techniques designed to stimulate the investigator rather than present any one set approach to the subject. Interested parties are encouraged to take the first step, namely, search for colleagues with expertise in other fields to become familiar with the rich data generated by antimicrobial surveillance and other research programs from the viewpoint of their individual specialities and search for novel aspects that will shed light on a problem that will only become more critical over the coming years. Data mining is one approach that may offer novel insights into our understanding of resistance, and ultimately may result in providing potential solutions to slow the rate of resistance against organisms of medical and environmental importance.
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Poupard, J.A., Gagnon, R.C., Stanhope, M.J. (2005). Data Mining to Discover Emerging Patterns of Antimicrobic Resistance. In: Gould, I.M., van der Meer, J.W.M. (eds) Antibiotic Policies. Springer, Boston, MA. https://doi.org/10.1007/0-387-22852-7_23
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