Skip to main content

Advertisement

SpringerLink
Log in

A fuzzy taxonomy for e-Health projects

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Evaluating the impact of Information Technology (IT) projects represents a problematic task for policy and decision makers aiming to define roadmaps based on previous experiences. Especially in the healthcare sector IT can support a wide range of processes and it is difficult to analyze in a comparative way the benefits and results of e-Health practices in order to define strategies and to assign priorities to potential investments. A first step towards the definition of an evaluation framework to compare e-Health initiatives consists in the definition of clusters of homogeneous projects that can be further analyzed through multiple case studies. However imprecision and subjectivity affect the classification of e-Health projects that are focused on multiple aspects of the complex healthcare system scenario. In this paper we apply a method, based on advanced cluster techniques and fuzzy theories, for validating a project taxonomy in the e-Health sector. An empirical test of the method has been performed over a set of European good practices in order to define a taxonomy for classifying e-Health projects.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Aanestad M, Jensen TB (2011) Building nation-wide information infrastructures in healthcare through modular implementation strategies. J Strateg Inf Syst 20(2):161–176

    Article  Google Scholar 

  2. Anderson DT, Bezdek JC, Popescu M, Keller JM (2010) Comparing fuzzy probabilistic, and possibilistic partitions. IEEE Trans Fuzzy Syst 18:906–918

    Article  Google Scholar 

  3. Barlow J, Bayer S, Curry R (2006) Implementing complex innovations in fluid multi-stakeholder environments: experiences of ‘telecare’. Technovation 26:396–406

    Article  Google Scholar 

  4. Bates DW (2005) Physicians and ambulatory electronic health records. Health Aff 24(5):1180–1189

    Article  MathSciNet  Google Scholar 

  5. Bezdek JC (1981) Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum Press, New York

    Book  Google Scholar 

  6. Blei M, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J. Mach. Learn. Res. 3:993–1022

    MATH  Google Scholar 

  7. Campello RJGB (2007) A fuzzy extension of the rand index and other related indexes for clustering and classification assessment. Pattern Recogn Lett 28:833–841

    Article  Google Scholar 

  8. Cannon RL, Davè JV, Bezdek JC (1986) Efficient implementation of the fuzzy C-means clustering algorithm. IEEE Trans Pattern Anal Mach Intell 8:248–255

    Article  MATH  Google Scholar 

  9. Ciborra C, Braa K et al (2000) From control to drift: the dynamics of corporate information infrastructures. Oxford University Press, Oxford

    Google Scholar 

  10. Colubi A, González-Rodríguez G, Gil MA, Trutschnig W (2011) Nonparametric criteria for supervised classification of fuzzy data. Int J Approx Reason 52:1272–1282

    Article  MATH  Google Scholar 

  11. Coppi, R. (2003) The fuzzy approach to multivariate statistical analysis, Technical report, Dipartimento di Statistica, Probabilità e Statistiche Applicate, Sapienza Università di Roma, n. 11

  12. Coppi R, D’Urso P, Giordani P (2012) Fuzzy and possibilistic clustering models for fuzzy data. Comput Stat Data Anal 56:915–927

    Article  MathSciNet  MATH  Google Scholar 

  13. Coppi R, Giordani P, D’Urso P (2006) Component models for fuzzy data. Psychometrika 71:733–761

    Article  MathSciNet  Google Scholar 

  14. Dixon BE (2007) A roadmap for the adoption of e-Health. E-Serv J 5(3):3–13

    Article  Google Scholar 

  15. D’Urso P (2007) Clustering of fuzzy data. In: de Oliveira JV, Pedrycz W (eds) Advances in Fuzzy Clustering and Its Applications. Wiley, New York, pp 155–192

    Chapter  Google Scholar 

  16. D’Urso P, Giordani P (2006) A weighted fuzzy c-means clustering model for fuzzy data. Comput Stat Data Anal 50:1496–1523

    Article  MathSciNet  MATH  Google Scholar 

  17. European Commission (2008) Information Society and Media Directorate-Genaral. Expert Impact Assessment. http://kb.good-ehealth.org/search.do

  18. European Commission (2009) Good eHealth Report-eHealth in Action Good Practice in European Countries. Office for Official Publications of the European Communities, Luxembourg

    Google Scholar 

  19. Everitt BS, Landau S, Leese M (2001) Cluster analysis, 4th edn. Arnold Press, London

    MATH  Google Scholar 

  20. Fadili MJ, Ruan S, Bloyet D, Mazoyer B (2001) On the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time series. Med Image Anal 5:55–67

    Article  Google Scholar 

  21. Fitterer R, Mettler T, Rohner P, Winter R (2011) Taxonomy for multi-perspective assessment of the value of health information systems. Int J Healthc Technol Manag 12(1):45–61

    Article  Google Scholar 

  22. Glaser BG, Strauss AL (1967) The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine Publishing Company, Chicago

    Google Scholar 

  23. González-Rodríguez G, Colubi A, Gil MA (2012) Fuzzy data treated as functional data: a one-way ANOVA test approach, Comput Stat Data Anal. (in press)

  24. Graaff AJ, Engelbrecht AP (2012) Clustering data in stationary environments with a local network neighborhood artificial immune system. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0041-0

    Google Scholar 

  25. Gregor S (2006) The nature of theory in information systems. MIS Q 30(3):611–642

    Google Scholar 

  26. Guo G, Chen S, Chen L (2012) Soft subspace clustering with an improved feature weight self-adjustment mechanism. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0038-8

    Google Scholar 

  27. Hall LO, Bensaid AM, Clarke LP (1992) A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 3:672–682

    Article  Google Scholar 

  28. Hanseth O, Aanestad M (2003) Design as bootstrapping. On the evolution of ICT network in healthcare. Methods Inf Med 42:385–391

    Google Scholar 

  29. Hanseth O, Lyytinen K (2010) Design theory for dynamic complexity in information infrastructures: the case of building internet. J Inf Technol 25:1–19

    Article  Google Scholar 

  30. Hawgood J, Land F (1988) A multivalent approach to information systems assessment. In: Bjorn-Andersen N, Davis GB (eds) Information Systems Assessment: Issues and Challenges. North Holland, Amsterdam, pp 103–124

    Google Scholar 

  31. Heiser WJ, Groenen PJF (1997) Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima. Psychometrika 62:63–83

    Article  MathSciNet  MATH  Google Scholar 

  32. Herriott RE, Firestone WA (1983) Multisite qualitative policy research: optimizing description and generalizability. Educ Res 12:14–19

    Google Scholar 

  33. Hung WL, Yang MS (2005) Fuzzy clustering on LR-type fuzzy numbers with an application in Taiwanese tea evaluation. Fuzzy Sets Syst 150:561–577

    Article  MathSciNet  MATH  Google Scholar 

  34. Hwang H, DeSarbo WS, Takane Y (2007) Fuzzy clusterwise generalized structured component analysis. Psychometrika 72:181–198

    Article  MathSciNet  MATH  Google Scholar 

  35. Irani Z, Love PED (2002) Developing a frame of reference for ex-ante IT/IS investment evaluation. Eur J Inf Syst 11(1):74–82

    Article  Google Scholar 

  36. Irani Z, Love PED (2008) Evaluating Information Systems: Public and Private Sector. Butterworth-Heinemann, Oxford

    Google Scholar 

  37. Irani Z, Sharif A, Love PED, Kahraman C (2002) Applying concepts of fuzzy logic cognitive mapping to model: the IT/IS investment evaluation process. Int J Prod Econ 75:199–211

    Article  Google Scholar 

  38. Lafky DB, Tulu B, Horan TA (2006) A User-driven approach to personal health records. Commun Assoc Inf Syst 17(46):1028–1041

    Google Scholar 

  39. Liang J, Song W (2012) Clustering based on Steiner points. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0047-7

    Google Scholar 

  40. Mac Queen JB (1967) Some methods for classification and analysis of multivariate observations. Proc Fifth Berkeley Symp Math Stat Prob 2:281–297

    MathSciNet  Google Scholar 

  41. Maharaj EA, D’Urso P (2011) Fuzzy clustering of time series in the frequency domain. Inf Sci 181:1187–1211

    Article  MATH  Google Scholar 

  42. Mantzana V, Themistocleous M, Irani Z, Morabito V (2007) Identifying healthcare actors involved in the adoption of information systems. Eur J Inf Syst 16(1):91–102

    Article  Google Scholar 

  43. McBratney AB, Moore AW (1985) Application of fuzzy sets to climatic classification. Agric For Meteorol 35:165–185

    Article  Google Scholar 

  44. McKelvey B (1982) Organizational Systematics—Taxonomy, Evolution, Classification, University of California Press, Berkeley, CA

  45. Menachemi N, Burke DE, Ayers D (2004) Factors affecting the adoption of telemedicine—a multiple adopter perspective. J Med Syst 28(6):617–632

    Article  Google Scholar 

  46. Mitchell J (2000) Increasing the cost-effectiveness of telemedicine by embracing e-Health. J Telemed Telecare 6:S16–S19

    Article  Google Scholar 

  47. Nagendran S, Moores D, Spooner R, Triscott J (2000) Is telemedicine a subset of medical informatics? J Telemed Telecare 6(Suppl. 2):50–51

    Article  Google Scholar 

  48. Ozkan I, Turksen IB (2007) Upper and lower values for the level of fuzziness in FCM. Inf Sci 177:5143–5152

    Article  MATH  Google Scholar 

  49. Pal NR, Bezdek JC (1995) On cluster validity for the fuzzy c-means model. IEEE Trans Fuzzy Syst 3:370–379

    Article  Google Scholar 

  50. Sinova B, Gil MA, Colubi A, Van Aelst S (2012) The median of a random fuzzy number. The 1-norm distance approach, Fuzzy Sets Syst. (in press)

  51. Smithson S, Hirschheim R (1998) Analysing information systems evaluation: another look at an old problem. Eur J Inf Syst 7(3):158–174

    Article  Google Scholar 

  52. Soreson JA, Wang X (1996) ROC methods for evaluation of fMRI techniques. Magn Res Med 36:737–744

    Article  Google Scholar 

  53. Spagnoletti P, Albano V, Caccetta E, Tarquini R, D’Atri A (2011) “Supporting policy definition in the e-Health domain: a QCA based method”. In: HEALTHINF—International conference on health informatics, 26–29 January, Roma, Italy

  54. Stockdale R, Standing C, Love PED, Irani Z (2008) Revisiting the content, context and process of IS evaluation. In: Irani Z, Love PED (eds) Evaluating Information Systems, Public and Private Sector. Butterworth-Heinemann, Oxford, pp 35–45

    Chapter  Google Scholar 

  55. Wedel M, Kamakura WA (1998) Market segmentation: Conceptual and methodological foundations. Kluwer Academic, Boston

    Google Scholar 

  56. Wilson V (2003) Asynchronous health care communication. Commun ACM 46(6):79–84

    Article  Google Scholar 

  57. Yin RK (2009) Case Study Research: Design and Methods, 4th edn. SAGE Publications, California

    Google Scholar 

  58. Yusof MM, Kuljis J, Papazafeiropoulou A, Stergioulas LK (2008) An evaluation framework for health information systems: human, organization and technology-fit factors (HOT-fit). Int J Med Inform 77(6):386–398

    Article  Google Scholar 

  59. Yusof MM, Papazafeiropoulou A, Paul RJ, Stergioulas LK (2008) Investigating evaluation frameworks for health information systems. Int J Med Inform 77(6):377–385

    Article  Google Scholar 

  60. Zadeh LA (2005) Toward a generalized theory of uncertainty (GTU)—an outline. Inf Sci 172:1–40

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

We wish to thank the referees and the Editor for their useful comments and suggestions which helped to improve the quality and presentation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Social Sciences, Sapienza-University of Rome, Rome, Italy

    Pierpaolo D’Urso

  2. Department of Political Science, LUISS Guido Carli University, Rome, Italy

    Livia De Giovanni

  3. CeRSI-LUISS Guido Carli University, Rome, Italy

    Paolo Spagnoletti

Authors
  1. Pierpaolo D’Urso
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Livia De Giovanni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paolo Spagnoletti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pierpaolo D’Urso.

Rights and permissions

Reprints and permissions

About this article

Cite this article

D’Urso, P., De Giovanni, L. & Spagnoletti, P. A fuzzy taxonomy for e-Health projects. Int. J. Mach. Learn. & Cyber. 4, 487–504 (2013). https://doi.org/10.1007/s13042-012-0118-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-012-0118-4

Keywords

Search

Navigation