Development of a clinical decision support system for antibiotic management in a hospital environment | Progress in Artificial Intelligence Skip to main content

Advertisement

Springer Nature Link
Log in

Development of a clinical decision support system for antibiotic management in a hospital environment

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

Abstract

The rise of infections caused by multidrug-resistant bacteria has become a very important issue for health institutions around the world, urging them to a more appropriate use of antibiotics. Clinical decision support systems have a very important role in this area. We propose the development of a clinical decision support system focused on the antibiotic stewardship program implemented in hospitals. The need for a multi-user perspective, both reactive and proactive behaviours, and the use of many heterogeneous knowledge sources are identified as the main requirements that differentiate this clinical scenario from a decision support point of view. We show that a combination of production rules, ontologies, workflow modelling and subgroup discovery techniques could be used to fulfil these requirements. Finally, we describe a platform on which these techniques will be developed and tested.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Clatworthy, A.E., Pierson, E., Hung, D.T.: Targeting virulence: a new paradigm for antimicrobial therapy. Nat. Chem. Biol. 3(9), 541–548 (2007)

    Article  Google Scholar 

  2. Piddock, L.J.: The crisis of no new antibiotics-what is the way forward? Lancet Infect. Dis. 12(3), 249–253 (2012)

    Article  Google Scholar 

  3. Spellberg, B.: The antibiotic crisis: can we reverse 65 years of failed stewardship? Arch. Intern. Med. 171(12), 1080–1081 (2011)

    Article  Google Scholar 

  4. Centers for Disease Control and Prevention.: Antibiotic resistance threats in the United States (2013)

  5. European Centre for Disease prevention and Control, European Medicines Agency.: The bacterial challenge: time to react (2009)

  6. World Health Organization. Antimicrobial resistance: global report on surveillance—summary (2014). http://apps.who.int/iris/handle/10665/112642

  7. Doron, S., Davidson, L.E.: Antimicrobial stewardship. Mayo Clinic Proc. 86(11), 1113–1123 (2011)

    Article  Google Scholar 

  8. Magill, S.S., Edwards, J.R., Bamberg, W., Beldavs, Z.G., Dumyati, G., Kainer, M.A., Lynfield, R., Maloney, M., McAllister-Hollod, L., Nadle, J., Ray, S.M., Thompson, D.L., Wilson, L.E., Fridkin, S.K.: Multistate point-prevalence survey of health care-associated infections. N. Engl. J. Med. 370(13), 1198–1208 (2014)

    Article  Google Scholar 

  9. Nathan, C., Cars, O.: Antibiotic resistance—problems, progress, and prospects. N. Engl. J. Med. 371(19), 1761–1763 (2014)

    Article  Google Scholar 

  10. Carling, P., Fung, T., Killion, A., Terrin, N., Barza, M.: Favorable impact of a multidisciplinary antibiotic management program conducted during 7 years. Infect. Control Hosp. Epidemiol. 24(9), 699–706 (2003)

    Article  Google Scholar 

  11. Schentag, J.J., Ballow, C.H., Fritz, A.L., Paladino, J.A., Williams, J.D., Cumbo, T.J., Ali, R.V., Galletta, V.A., Gutfeld, M.B., Adelman, M.H.: Changes in antimicrobial agent usage resulting from interactions among clinical pharmacy, the infectious disease division, and the microbiology laboratory. Diagn. Microbiol. Infect. Dis. 16(3), 255–264 (1993)

    Article  Google Scholar 

  12. Sackett, D.L.: Evidence-based Medicine. In: Armitage, P., Colton, T. (eds.) Encyclopedia of Biostatistics. John Wiley & Sons Ltd, Chichester (2005)

    Google Scholar 

  13. Barnett, G.O., Famiglietti, K.T., Kim, R.J., Hoffer, E.P., Feldman, M.J.: Dxplain on the internet. In: Proceedings of the AMIA Symposium, p. 607. American Medical Informatics Association (1998)

  14. Kuperman, G.J., Gardner, R.M., Pryor, T.A.: HELP: A Dynamic Hospital Information System. Computers and Medicine. Springer, New York (1991)

  15. Ennis, J., Gillen, D., Rubenstein, A., Worcester, E., Brecher, M.E., Asplin, J., Coe, F.: Clinical decision support improves physician guideline adherence for laboratory monitoring of chronic kidney disease: a matched cohort study. BMC Nephrol. 16(1), 163 (2015)

    Article  Google Scholar 

  16. Trafton, J.A., Martins, S.B., Michel, M.C., Wang, D., Tu, S.W., Clark, D.J., Elliott, J., Vucic, B., Balt, S., Clark, M.E., Sintek, C.D., Rosenberg, J., Daniels, D., Goldstein, M.K.: Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain. Implement. Sci. 5(1), 26 (2010)

    Article  Google Scholar 

  17. Calloway, S., Akilo, H.A., Bierman, K.: Impact of a clinical decision support system on pharmacy clinical interventions, documentation efforts, and costs. Hosp. Pharm. 48(9), 744–752 (2013)

    Article  Google Scholar 

  18. Forrest, G.N., Van Schooneveld, T.C., Kullar, R., Schulz, L.T., Duong, P., Postelnick, M.: Use of electronic health records and clinical decision support systems for antimicrobial stewardship. Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am. 59 Suppl 3(Suppl 3), S122–S133 (2014)

  19. Pestotnik, S.L.: Expert clinical decision support systems to enhance antimicrobial stewardship programs: insights from the society of infectious diseases pharmacists. Pharmacotherapy 25(8), 1116–25 (2005)

    Article  Google Scholar 

  20. Belle, A., Kon, M.A., Najarian, K.: Biomedical informatics for computer-aided decision support systems: a survey. Sci. World J. 2013 769, 639 (2013)

    Google Scholar 

  21. Mitchell, J.A., Gerdin, U., Lindberg, D.A.B., Lovis, C., Martin-Sanchez, F.J., Miller, R.A., Shortliffe, E.H., Leong, T.Y.: 50 years of informatics research on decision support: what’s next. Methods Inform. Med. 50(6), 525–535 (2011)

    Article  Google Scholar 

  22. Sittig, D.F., Wright, A., Osheroff, J.A., Middleton, B., Teich, J.M., Ash, J.S., Campbell, E.M., Bates, D.W.: Grand challenges in clinical decision support. J. Biomed. Inform. 41(2), 387–392 (2008)

    Article  Google Scholar 

  23. Greenes, R.A.: Chapter 1—definition, scope, and challenges. In: Greenes, R.A. (ed.) Clinical Decision Support, 2nd edn, pp. 3–47. Academic, Oxford (2014)

    Chapter  Google Scholar 

  24. Greenes, R.A.: Chapter 3—features of computer-based clinical decision support. In: Greenes, R.A. (ed.) Clinical Decision Support, 2nd edn, pp. 111–144. Academic, Oxford (2014)

    Chapter  Google Scholar 

  25. Cimino, J.J., Elhanan, G., Zeng, Q.: Supporting infobuttons with terminological knowledge. Proceedings: a conference of the American Medical Informatics Association/... AMIA Annual Fall Symposium. AMIA Fall Symposium, pp. 528–532 (1997)

  26. Nadkarni, P.M., Ohno-Machado, L., Chapman, W.W.: Natural language processing: an introduction. J. Am. Med. Inform. Assoc. 18(5), 544–551 (2011)

    Article  Google Scholar 

  27. de Dombal, F.T., Leaper, D.J., Staniland, J.R., McCann, A.P., Horrocks, J.C.: Computer-aided diagnosis of acute abdominal pain. BMJ 2(5804), 9–13 (1972)

    Article  Google Scholar 

  28. Barnett, G.O.: DXplain. JAMA 258(1), 67 (1987)

    Article  Google Scholar 

  29. Shortliffe, E.: Computer-based medical consultations: MYCIN. American Elsevier, USA (1976)

    Google Scholar 

  30. Warner, H.R., Haug, P., Bouhaddou, O., Lincoln, M., Warner, H., Sorenson, D., Williamson, J.W., Fan, C.: ILIAD as an Expert Consultant to Teach Differential Diagnosis. Proceedings of the Annual Symposium on Computer Application in Medical Care, pp. 371–376 (1988)

  31. Peleg, M.: Computer-interpretable clinical guidelines: a methodological review. J. Biomed. Inform. 46(4), 744–763 (2013)

    Article  Google Scholar 

  32. Combi, C., Keravnou-Papailiou, E., Shahar, Y.: Temporal Information Systems in Medicine. Springer, Boston (2010)

    Book  Google Scholar 

  33. 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)

    Article  Google Scholar 

  34. 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)

  35. 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)

    Article  Google Scholar 

  36. Lovis, C., Colaert, D., Stroetmann, V.N.: DebugIT for patient safety—improving the treatment with antibiotics through multimedia data mining of heterogeneous clinical data. Stud. Health Technol. Inform. 136, 641–646 (2008)

    Google Scholar 

  37. Schober, D., Boeker, M., Bullenkamp, J., Huszka, C., Depraetere, K., Teodoro, D., Nadah, N., Choquet, R., Daniel, C., Schulz, S.: The DebugIT core ontology: semantic integration of antibiotics resistance patterns. Stud. Health Technol. Inform. 160(Pt 2), 1060–1064 (2010)

    Google Scholar 

  38. Teodoro, D., Pasche, E., Gobeill, J., Emonet, S., Ruch, P., Lovis, C.: Building a transnational biosurveillance network using semantic web technologies: requirements, design, and preliminary evaluation. J. Med. Internet Res. 14(3), e73 (2012)

    Article  Google Scholar 

  39. Berner, E.: Clinical decision support systems: state of the art. Agency Healthc. Res. Qual. 09, 4–20 (2009)

    Google Scholar 

  40. Wright, A., Goldberg, H., Hongsermeier, T., Middleton, B.: A description and functional taxonomy of rule-based decision support content at a large integrated delivery network. J. Am. Med. Inform. Assoc. 14(4), 489–496 (2007)

    Article  Google Scholar 

  41. Musen, M.A., Shahar, Y., Shortliffe, E.H.: Clinical decision-support systems. Biomed. Inform. 30, 698–736 (2006)

    Article  Google Scholar 

  42. Busto, J., Varas, J.: Knowledge enabled services (KES) for decision support in control rooms. CESADS (KES) case study at ESA/ESOC. In: Proceedings of the 10th International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS), pp. 10–14, Geneva (2005)

  43. Hinkelmann, K., Probst, F., Thönssen, B.: Agile process management: framework and methodology. AAAI Spring Symposium—Technical Report SS-06-06(Imi), pp. 33–41 (2006)

  44. Bragaglia, S., Chesani, F., Ciampolini, A., Mello, P., Montali, M., Sottara, D.: An hybrid architecture integrating forward rules with fuzzy ontological reasoning. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6076 LNAI, pp. 438–445 (2010)

  45. 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, pp. 204–211. Leipzig, Germany (2012)

  46. 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. Inform. 180, 300–304 (2012)

    Google Scholar 

  47. Leclercq, R., Cantón, R., Brown, D.F.J., Giske, C.G., Heisig, P., Macgowan, aP, Mouton, J.W., Nordmann, P., Rodloff, aC, 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)

    Article  Google Scholar 

  48. Motik, B., Patel-Schneider, P.F., Parsia, B., Bock, C., Fokoue, A., Haase, P., Hoekstra, R., Horrocks, I., Ruttenberg, A., Sattler, U., Smith, M.: OWL 2 web ontology language—structural specification and functional-style syntax, 2nd edn, pp. 1–133. Online (2012)

  49. Horridge, M., Bechhofer, S.: The OWLAPI: a Java API for OWL ontologies. Semant. Web 2(1), 11–21 (2011)

    Google Scholar 

  50. Stanford Center for Biomedical Informatics Research Stanford University, S.o.M.: Protégé Web site. http://protege.stanford.edu/

  51. Proctor, M., Neale, M., Lin, P., Frandsen, M.: Drools documentation. Technical report, pp. 1–297 (2008)

  52. Croset, S.: OWL version of the anatomical therapeutic chemical classification system. http://www.ebi.ac.uk/Rebholz-srv/atc/

  53. WHO Collaborating Centre for Drug Statistics Methodology: Guidelines for ATC classification and DDD assignment 2013. Oslo (2012). http://www.whocc.no/filearchive/publications/1_2013guidelines.pdf

  54. NCBI Taxonomy Database website. http://www.ncbi.nlm.nih.gov/taxonomy

  55. Federhen, S.: The NCBI taxonomy database. Nucleic Acids Res. 40(Database issue), D136–D143 (2012)

  56. Schärli, N., Ducasse, S., Nierstrasz, O., Black, a: Traits: composable units of behaviour. Lecture Notes Comput. Sci. 2743, 248–274 (2003)

    Article  Google Scholar 

  57. Shearer, R., Motik, B., Horrocks, I.: HermiT: a highly-efficient OWL reasoner. Complexity 432, 10 (2008)

    Google Scholar 

  58. Clayton, P.D., Pryor, T.A., Wigertz, O.B., Hripcsak, G.: Issues and structures for sharing medical knowledge among decision-making systems: the 1989 Arden Homestead Retreat. In: Proceedings of the Annual Symposium on Computer Application in Medical Care. American Medical Informatics Association, pp. 116 (1989)

  59. Adlassnig, K.P., Blacky, A., Koller, W.: Artificial-intelligence-based hospital-acquired infection control. Stud. Health Technol. Inform. 149, 103–110 (2009)

    Google Scholar 

  60. Jung, C.Y., Sward, Ka, Haug, P.J.: Executing medical logic modules expressed in ArdenML using Drools. J. Am. Med. Inform. Assoc. 19(4), 533–536 (2012)

    Article  Google Scholar 

  61. Peleg, M., Boxwala, aa, Bernstam, E., Tu, S., Greenes, Ra, Shortliffe, E.H.: Sharable representation of clinical guidelines in GLIF: relationship to the Arden Syntax. J. Biomed. Inform. 34(3), 170–181 (2001)

    Article  Google Scholar 

  62. Shortliffe, E.H., Scott, A.C., Bischoff, M.B., Campbell, A.B., Van Melle, W., Jacobs, C.D.: ONCOCIN: an expert system for oncology protocol management. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence - Volume 2 (IJCAI’81), pp. 876–881. Morgan Kaufmann Publishers Inc., San Francisco, CA (1981)

  63. Musen, M.A., Carlson, R.W., Fagan, L.M., Deresinski, S.C., Shortliffe, E.H.: T-HELPER: automated support for community-based clinical research. Proceedings/the... Annual Symposium on Computer Application [sic] in Medical Care. Symposium on Computer Applications in Medical Care, pp. 719–23 (1992)

  64. Mulyar, N., van der Aalst, W.M.P., Peleg, M.: A pattern-based analysis of clinical computer-interpretable guideline modeling languages. J. Am. Med. Inform. Assoc. JAMIA 14(6), 781–787 (2007)

    Article  Google Scholar 

  65. Osheroff, J.A., Teich, J., Levick, D., Saldana, L., Velasco, F., Sittig, D., Rogers, K., Jenders, R.: Improving Outcomes with Clinical Decision Support: An Implementer’s Guide. Healthcare Information and Management Systems Society, Chicago (2012)

  66. Tu, S.W., Campbell, J.R., Glasgow, J., Nyman, Ma., McClure, R., McClay, J., Parker, C., Hrabak, K.M., Berg, D., Weida, T., Mansfield, J.G., Musen, Ma., Abarbanel, R.M.: The SAGE guideline model: achievements and overview. J. Am. Med. Inform. Assoc. 14(5), 589–598 (2007)

    Article  Google Scholar 

  67. Rybak, M., Lomaestro, B., Rotschafer, J.C., Moellering, R., Craig, W., Billeter, M., Dalovisio, J.R., Levine, D.P., Reilly, C.: Therapeutic monitoring of vancomycin in adult patients: a consensus review of the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, and the Society of Infectious Diseases Pharmacists. Am. J. Health Syst. Pharm. 66(1), 82–98 (2009)

    Article  Google Scholar 

  68. Landers, T., Apte, M., Hyman, S., Furuya, Y., Glied, S., Larson, E.: A comparison of methods to detect urinary tract infections using electronic data. Jt. Comm. J. Qual. Patient Saf. Jt. Comm. Res. 36(9), 411–417 (2010)

    Google Scholar 

  69. Beaudoin, M., Kabanza, F., Nault, V., Valiquette, L.: An antimicrobial prescription surveillance system that learns from experience. AI Mag. 35(1), 15–25 (2014)

    Google Scholar 

  70. Ma, L., Tsui, F., Hogan, W.: A framework for infection control surveillance using association rules. AMIA annual symposium..., pp. 410–414 (2003)

  71. Lamma, E., Manservigi, M., Mello, P., Nanetti, A., Riguzzi, F., Storari, S.: The automatic discovery of alarm rules for the validation of microbiological data. In: Proceedings of Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP) (2001)

  72. Samore, M.H., Bateman, K., Alder, S.C., Hannah, E., Donnelly, S., Stoddard, G.J., Haddadin, B., Rubin, M.A., Williamson, J., Stults, B., Rupper, R., Stevenson, K.: Clinical decision support and appropriateness of antimicrobial prescribing: a randomized trial. JAMA 294(18), 2305–2314 (2005)

    Article  Google Scholar 

  73. Leibovici, L., Paul, M., Nielsen, A.D., Tacconelli, E., Andreassen, S.: The TREAT project: decision support and prediction using causal probabilistic networks. Int. J. Antimicrob. Agents 30, 93–102 (2007)

    Article  Google Scholar 

  74. Fox, B.C., Shenk, G., Peterson, D., Spiegel, C.A., Maki, D.G.: Choosing more effective antimicrobial combinations for empiric antimicrobial therapy of serious gram-negative rod infections using a dual cross-table antibiogram. Am. J. Infect. Control 36(3), S57–S61 (2008)

  75. Herrera, F., Carmona, C.J., González, P., del Jesus, M.J.: An overview on subgroup discovery: foundations and applications. Knowl. Inform. Syst. 29(3), 495–525 (2011)

    Article  Google Scholar 

  76. Wrobel, S.: An algorithm for multi-relational discovery of subgroups. Lecture Notes Comput. Sci. 1263(1997), 78–87 (1997)

    Article  Google Scholar 

  77. Umek, L., Zupan, B., Toplak, M., Morin, A., Chauchat, J.H., Makovec, G., Smrke, D.: Subgroup Discovery in Data Sets with Multi-dimensional Responses: A Method and a Case Study in Traumatology, pp. 265–274. Springer, Berlin (2009)

    Google Scholar 

  78. Abu-Hanna, A., Nannings, B., Dongelmans, D., Hasman, A.: PRIM versus CART in subgroup discovery: when patience is harmful. J. Biomed. Inform. 43(5), 701–708 (2010)

    Article  Google Scholar 

  79. Gamberger, D., Lavrač, N., Krstačić, G.: Active subgroup mining: a case study in coronary heart disease risk group detection. Artif. Intell. Med. 28(1), 27–57 (2003)

    Article  Google Scholar 

  80. Carmona, C.J., Chrysostomou, C., Seker, H., del Jesus, M.J.: Fuzzy rules for describing subgroups from Influenza A virus using a multi-objective evolutionary algorithm. Appl. Soft Comput. 13(8), 3439–3448 (2013)

    Article  Google Scholar 

  81. García, A., Charte, F., González, P., Carmona, C.J., del Jesús, M.J.: Usando Algoritmos de Descubrimiento de Subgrupos en R: El Paquete SDR. In: Actas de la XVI Conferencia de la Asociación Española para la Inteligencia Artificial, CAEPIA 2015, Albacete, Noviembre 9–12, 2015, pp. 739–748 (2015)

  82. del Jesus, M.J., Gonzalez, P., Herrera, F.: Multiobjective genetic algorithm for extracting subgroup discovery fuzzy rules. In: 2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, pp. 50–57. IEEE (2007)

  83. National Research Council.: Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. National Academies Press, Washington, D.C. (2009)

  84. Institute of Medicine: Health IT and Patient Safety: Building Safer Systems for Better Care. National Academies Press, Washington, D.C. (2012)

    Google Scholar 

  85. Rind, A.: Interactive information visualization to explore and query electronic health records. Foundations and Trends\(^{\textregistered }\) in Human–Computer Interaction. 5(3), 207–298 (2013)

  86. Chittaro, L., Combi, C., Trapasso, G.: Data mining on temporal data: a visual approach and its clinical application to hemodialysis. J. Vis. Lang. Comput. 14(6), 591–620 (2003)

    Article  Google Scholar 

  87. Pinciroli, F., Portoni, L., Combi, C., Violante, F.F.: WWW-based access to object-oriented clinical databases: the KHOSPAD project. Comput. Biol. Med. 28(5), 531–552 (1998)

    Article  Google Scholar 

  88. Shahar, Y., Goren-Bar, D., Boaz, D., Tahan, G.: Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions. Artif. Intell. Med. 38(2), 115–135 (2006)

    Article  Google Scholar 

  89. Klimov, D., Shahar, Y., Taieb-Maimon, M.: Intelligent interactive visual exploration of temporal associations among multiple time-oriented patient records. Methods Inform. Med. 48(3), 254–262 (2009)

    Article  Google Scholar 

  90. Wang, T.: Interactive visualization techniques for searching temporal categorical data. Ph.D. thesis, University of Maryland (2010)

  91. Lins, L., Heilbrun, M., Freire, J., Silva, C.: Viscaretrails: visualizing trails in the electronic health record with timed word trees, a pancreas cancer use case. In: AMIA Workshop on Visual Analytics in HealthCare (2013)

  92. Beilken, C., Spenke, M.: Visual, interactive data mining with InfoZoom—the medical data set. In: Workshop notes on discovery challenge. In: Proceedings of the 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases, 1999 Sept 15–18, pp. 49–54. Prague (1999)

  93. Gustafson, T.L.: Practical risk-adjusted quality control charts for infection control. Am. J. Infect. Control 28(6), 406–414 (2000)

    Article  Google Scholar 

  94. Garcia-caballero, H., Campos, M., Juarez, J.M., Palacios, F.: Visualization in clinical decision support system for antibiotic treatment. In: Actas de la XVI Conferencia de la Asociación Española para la Inteligencia Artificial, CAEPIA 2015, Albacete, Noviembre 9–12, 2015, pp. 71–80 (2015)

  95. Zhu, Y., Sun, L., Garbarino, A., Schmidt, C., Fang, J., Chen, J.: PathRings: a web-based tool for exploration of ortholog and expression data in biological pathways. BMC Bioinform. 16(1), 165 (2015)

  96. Lam, H., Bertini, E., Isenberg, P., Plaisant, C., Carpendale, S.: Empirical studies in information visualization: seven scenarios. IEEE Trans. Vis. Comput. Graph. 18(9), 1520–1536 (2012)

  97. White, T.: Hadoop: The Definitive Guide. O’Reilly Media Inc., Sebastopol (2012)

    Google Scholar 

  98. Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing. HotCloud’10, pp. 10–10. USENIX Association, Berkeley, CA, USA (2010)

  99. Health Level 7. HL7 Web site. http://www.hl7.org/

  100. Schreiber, G., Akkermans, H., Anjewierden, A., Dehoog, R., Shadbolt, N., Vandevelde, W., Wielinga, B.: Knowledge Engineering and Management: The CommonKADS Methodology. The MIT Press, Cambridge (1999)

    Google Scholar 

  101. Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., Grenning, J., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R.C., Mellor, S., Schwaber, K., Sutherland, J., Thomas, D.: Manifesto for Agile Software Development (2001). http://www.agilemanifesto.org/

  102. Dingsøyr, T., Nerur, S., Balijepally, V., Moe, N.B.: A decade of agile methodologies: towards explaining agile software development. J. Syst. Softw. 85(6), 1213–1221 (2012)

    Article  Google Scholar 

  103. Boussadi, A., Bousquet, C., Sabatier, B., Caruba, T., Durieux, P., Degoulet, P.: A business rules design framework for a pharmaceutical validation and alert system. Methods Inform. Med. 50(1), 36–50 (2011)

    Article  Google Scholar 

  104. Zacharias, V.: The agile development of rule bases. In: Barry, C. et al. (eds.) Information Systems Development: Challenges in Practice, Theory, and Education, vol. 1, pp. 93–104. Springer, Boston (2009)

  105. Beck, K.: Embracing change with extreme programming. Computer 32(10), 70–77 (1999)

    Article  Google Scholar 

  106. SNOMED-CT Website. http://www.ihtsdo.org/snomed-ct

  107. Carayon, P., Schoofs Hundt, A., Karsh, B.-T., Gurses, A.P., Alvarado, C.J., Smith, M., Flatley Brennan, P.: Work system design for patient safety: the SEIPS model. Qual. Saf. Heal. Care. 15, i50–i58 (2006)

  108. Donabedian, A.: Evaluating the quality of medical care. Milbank Meml. Fund Q. 44(3), 166 (1966)

    Article  Google Scholar 

  109. Kaplan, B.: Evaluating informatics applications-clinical decision support systems literature review. Int. J. Med. Inform. 64(1), 15–37 (2001)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Faculty, University of Murcia, Murcia, Spain

    Bernardo Cánovas-Segura, Manuel Campos, Antonio Morales & Jose M. Juarez

  2. University Hospital of Getafe, Madrid, Spain

    Francisco Palacios

Authors
  1. Bernardo Cánovas-Segura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel Campos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio Morales
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jose M. Juarez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Francisco Palacios
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bernardo Cánovas-Segura.

Additional information

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).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cánovas-Segura, B., Campos, M., Morales, A. et al. Development of a clinical decision support system for antibiotic management in a hospital environment. Prog Artif Intell 5, 181–197 (2016). https://doi.org/10.1007/s13748-016-0089-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-016-0089-x

Keywords

Search

Navigation