NDLI: Discovering validation rules from microbiological data

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Discovering validation rules from microbiological data

Content Provider	Springer Nature Link
Author	Lamma, Evelina Riguzzi, Fabrizio Storari, Sergio Mello, Paola Nanetti, Anna
Copyright Year	2003
Abstract	A huge amount of data is daily collected from clinical microbiology laboratories. These data concern the resistance or susceptibility of bacteria to tested antibiotics. Almost all microbiology laboratories follow standard antibiotic testing guidelines which suggest antibiotic test execution methods and result interpretation and validation (among them, those annually published by NCCLS2,3). Guidelines basically specify, for each species, the antibiotics to be tested, how to interpret the results of tests and a list of exceptions regarding particular antibiotic test results. Even if these standards are quite assessed, they do not consider peculiar features of a given hospital laboratory, which possibly influence the antimicrobial test results, and the further validation process.In order to improve and better tailor the validation process, we have applied knowledge discovery techniques, and data mining in particular, to microbiological data with the purpose of discovering new validation rules, not yet included in NCCLS guidelines, but considered plausible and correct by interviewed experts. In particular, we applied the knowledge discovery process in order to find (association) rules relating to each other the susceptibility or resistance of a bacterium to different antibiotics. This approach is not antithetic, but complementary to that based on NCCLS rules: it proved very effective in validating some of them, and also in extending that compendium. In this respect, the new discovered knowledge has lead microbiologists to be aware of new correlations among some antimicrobial test results, which were previously unnoticed. Last but not least, the new discovered rules, taking into account the history of the considered laboratory, are better tailored to the hospital situation, and this is very important since some resistances to antibiotics are specific to particular, local hospital environments.
Starting Page	123
Ending Page	133
Page Count	11
File Format	PDF
ISSN	02883635
Journal	New Generation Computing
Volume Number	21
Issue Number	2
e-ISSN	18827055
Language	English
Publisher	Springer-Verlag
Publisher Date	2003-01-01
Publisher Place	Tokyo
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Knowledge Discovery and Data mining Microbiology Knowledge Based Systems Knowledge Elicitation Artificial Intelligence (incl. Robotics) Computer Hardware Computer Systems Organization and Communication Networks Software Engineering/Programming and Operating Systems Computing Methodologies
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Networks and Communications Software Hardware and Architecture

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