Abstract
Prescribing the proper antibiotic against an infectious agent is crucial not only for the patient’s health but also for the community, due to fast development of new resistances in bacteria. To ensure the efficacy of the treatment, laboratory tests are performed on cultures of samples obtained from the patient, analysing the resistance patterns of the infectious microorganism against some antibiotics. In order to assist clinical microbiologists, the European Committee on Antimicrobial Susceptibility Testing proposes a catalogue of clinical rules to identify resistance patterns and clinical recommendations from the results of previous antibiotic susceptibility tests. The aim of our proposal is to automatise and evaluate this source of biomedical knowledge. To this end, we have implemented a knowledge module combining ontologies and production rules. This module was included in a clinical decision support system and evaluated using test results of 365 days. After its execution, \(20.9\,\%\) of the final antibiograms were new resistance patterns not covered by the laboratory tests, having \(44\,\%\) of them a high clinical evidence grade. These results could help clinicians to prescribe more efficient treatments against infections in the future.
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References
Bragaglia, S., Chesani, F., Ciampolini, A., Mello, P., Montali, M., Sottara, D.: An hybrid architecture integrating forward rules with fuzzy ontological reasoning. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010, Part I. LNCS, vol. 6076, pp. 438–445. Springer, Heidelberg (2010)
Busto, J., Varas, J.: Knowledge enabled services (KES) for decision support in control rooms. CESADS (KES) case study at ESA/ESOC. In: 10th International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS), pp. 10–14, Geneva (2005)
Cánovas-Segura, B., Campos, M., Morales, A., Juarez, J.M., Palacios, F.: Development of a clinical decision support system for antibiotic management in a hospital environment. Prog. Artif. Intell. 1–17 (2016). doi:10.1007/s13748-016-0089-x
Centers for Disease Control, Prevention: Antibiotic Resistance Threats in the United States (2013)
European Centre for Disease Prevention and Control, European Medicines Agency: The bacterial challenge: time to react (2009)
Federhen, S.: The NCBI taxonomy database. Nucleic Acids Res. 40(Database issue), D136–D143 (2012)
Horrocks, I., Patel-schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: a semantic web rule language combining OWL and RuleML. In: W3C Member Submission, 21 May 2004 (2004). https://www.w3.org/Submission/SWRL/
Lamma, E., Mello, P., Nanetti, A., Riguzzi, F., Storari, S., Valastro, G.: Artificial intelligence techniques for monitoring dangerous infections. IEEE Trans. Inform. Technol. Biomed. 10(1), 143–155 (2006)
Leclercq, R., Cantón, R., Brown, D.F.J., Giske, C.G., Heisig, P., Macgowan, A.P., Mouton, J.W., Nordmann, P., Rodloff, A.C., Rossolini, G.M., Soussy, C.J., Steinbakk, M., Winstanley, T.G., Kahlmeter, G.: EUCAST expert rules in antimicrobial susceptibility testing. Clin. Microbiol. Infect. 19(2), 141–160 (2013)
Lo, Y.S., Liu, C.T.: Development of a hospital-acquired infection surveillance information system by using service-oriented architecture technology. In: 2010 3rd International Conference on Computer Science and Information Technology, vol. 4, pp. 449–453. IEEE (2010)
W3C OWL Working Group: OWL 2 web ontology language document overview (Second Edition). In: Online, pp. 1–7 (2012). http://www.w3.org/TR/owl2-overview/
Proctor, M., Neale, M., Lin, P., Frandsen, M.: Drools documentation. Technical report, pp. 1–297 (2008)
Sottara, D., Bragaglia, S., Mello, P., Pulcini, D., Luccarini, L., Giunchi, D.: Ontologies, rules, workflow and predictive models: knowledge assets for an EDSS. In: 6th International Congress on Environmental Modelling and Software, iEMSs 2012 Proceedings, Leipzig, Germany, pp. 204–211 (2012)
Steurbaut, K., Colpaert, K., Gadeyne, B., Depuydt, P., Vosters, P., Danneels, C., Benoit, D., Decruyenaere, J., De Turck, F.: COSARA: integrated service platform for infection surveillance and antibiotic management in the ICU. J. Med. Syst. 36, 3765–3775 (2012)
Van Hille, P., Jacques, J., Taillard, J., Rosier, A., Delerue, D., Burgun, A., Dameron, O.: Comparing Drools and ontology reasoning approaches for telecardiology decision support. Stud. Health Technol. Inf. 180, 300–304 (2012)
WHO Collaborating Centre for Drug Statistics Methodology: Guidelines for ATC classification and DDD assignment 2013. Oslo (2012)
Zapantis, A., Lacy, M.K., Horvat, R.T., Grauer, D., Barnes, B.J., O’Neal, B., Couldry, R.: Nationwide antibiogram analysis using NCCLS M39-A guidelines. J. Clin. Microbiol. 43(6), 2629–2634 (2005)
Acknowledgments
This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the WASPSS project (Ref: TIN2013-45491-R) and by the European Fund for Regional Development (EFRD, FEDER).
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Cánovas-Segura, B., Campos, M., Morales, A., Juarez, J.M., Palacios, F. (2016). Clinical Decision Support Using Antimicrobial Susceptibility Test Results. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_23
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DOI: https://doi.org/10.1007/978-3-319-44636-3_23
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