A fuzzy taxonomy for e-Health projects

D'Urso, Pierpaolo and De Giovanni, Livia and Spagnoletti, Paolo (2012) A fuzzy taxonomy for e-Health projects. International Journal of Machine Learning and Cybernetics p. 1-18. ISSN 1868-8071. (In Press)

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
400Kb

Official URL: http://www.springer.com/engineering/computational+...

Abstract/Index

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.

References

Aanestad M, Jensen TB (2011) Building nation-wide information infrastructures in healthcare through modular implementation strategies. J Strateg Inf Syst 20(2): 161-176. Anderson, D.T., Bezdek, J.C., Popescu, M., Keller, J.M. (2010) Comparing Fuzzy, Probabilistic, and Possibilistic Partitions, IEEE Transaction on Fuzzy Systems, 18, 906-918. Barlow J, Bayer S, Curry R (2006) Implementing complex innovations in fluid multi-stakeholder environments: Experiences of ‘telecare’, Technovation 26, 396–406. Bates DW (2005) Physicians and Ambulatory Electronic Health Records. Health Aff: 24/5:1180-1189 Bezdek J.C. (1981) Pattern Recognition with Fuzzy Objective Function Algorithm, Plenum Press. Blei M., Ng A.Y., Jordan M.I. (2003) Latent Dirichlet allocation. J. Mach. Learn. Res., 3, 993–1022. Campello, R.J.G.B. (2007) A Fuzzy Extension of the Rand Index and Other Related Indexes for Clustering and Classification Assessment, Pattern Recognition Letters, 28, 833 – 841. Cannon, R.L., Davè, J.V., Bezdek, J.C. (1986) Efficient implementation of the fuzzy C-means clustering algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 248–255. Ciborra C, Braa K, et al (2000) From Control to Drift: The Dynamics of Corporate Information Infrastructures. Oxford University Press, Oxford Colubi, A., González-Rodríguez, G., Gil, M.A., Trutschnig, W. (2011) Nonparametric criteria for supervised classification of fuzzy data, International Journal of Approximate Reasoning 52, 1272–1282. 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. Coppi, R., D’Urso, P., Giordani, P. (2012) Fuzzy and Possibilistic Clustering Models for Fuzzy Data, Computational Statistics and Data Analysis, 915-927. Coppi, R., Giordani, P., D’Urso, P. (2006) Component Models for Fuzzy Data, Psychometrika, 71, 733–761. Dixon BE (2007) A roadmap for the adoption of e-Health. E-Serv J 5(3):3-13 D'Urso, P. (2007) Clustering of fuzzy data, in Advances in Fuzzy Clustering and Its Applications (eds. de Oliveira J.V., Pedrycz W.), J. Wiley and Sons, 155–192. D'Urso, P., Giordani, P. (2006) A weighted fuzzy c-means clustering model for fuzzy data, Computational Statistics and Data Analysis, 50, 1496–1523. European Commission (2008) Information Society and Media Directorate-Genaral. Expert Impact Assessment. http://kb.good-ehealth.org/search.do European Commission (2009) Good eHealth Report-eHealth in Action Good Practice in European Countries, Luxembourg: Office for Official Publications of the European Communities Everitt, B.S., Landau, S., Leese, M. (2001) Cluster analysis (4th ed.). London: Arnold Press. Fadili, M.J., 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, Medical Image Analysis, 5, 55-67. 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 Glaser, B. G., and Strauss, A. L. (1967) The Discovery of Grounded Theory: Strategies for Qualitative Research, Aldine Publishing Company, Chicago González-Rodríguez, G., Colubi, A, Gil, M.A. (2012) Fuzzy data treated as functional data: A one-way ANOVA test approach, Computational Statistics and Data Analysis, in press. Graaff, A.J., Engelbrecht, A.P. (2012) Clustering data in stationary environments with a local network neighborhood artificial immune system, International Journal of Machine Learning and Cybernetics, DOI: 10.1007/s13042-011-0041-0. Gregor S,(2006) The nature of theory in information systems, MIS Q 30 (3):611–642 Guo, G., Chen, S., Chen, L. (2012) Soft subspace clustering with an improved feature weight self-adjustment mechanism, International Journal of Machine Learning and Cybernetics, DOI: 10.1007/s13042-011-0038-8. Hall, L.O., Bensaid, A.M., Clarke, L.P. (1992) A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain, IEEE Transactions on Neural Networks, 3, 672-682. Hanseth O, Aanestad M (2003) Design as bootstrapping. On the evolution of ICT network in healthcare. Methods Inf Med 42:385–391 Hanseth O, Lyytinen K (2010) Design theory for dynamic complexity in information infrastructures: the case of building internet. J Inf Technol 25:1–19 Hawgood, J and Land, F (1988) A multivalent approach to information systems assessment. In Information Systems Assessment: Issues and Challenges (Bjorn-Andersen N and Davis GB, Eds), pp 103-124, North Holland, Amsterdam Heiser, W.J., Groenen, P.J.F. (1997) Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima, Psychometrika, 62, 63-83. Herriott, R. E., and W.A. Firestone, 1983, Multisite qualitative policy research: Optimizing description and generalizability. Educational Researcher, 12, 14 -19. Hung, W.L., Yang M.S. (2005) Fuzzy clustering on LR-type fuzzy numbers with an application in Taiwanese tea evaluation. Fuzzy Sets and Systems, 150, 561-577. Hwang, H., DeSarbo, W.S., Y. Takane (2007) Fuzzy clusterwise generalized structured component analysis, Psychometrika, 72, 181-198. 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 Irani Z, Love PED (2008) Evaluating Information Systems: Public and Private Sector. Butterworth-Heinemann, Oxford 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 ProdEcon 75:199-211 Lafky DB, Tulu B, Horan TA (2006) A User-driven approach to personal health records. Commun Assoc Inf Syst 17(46):1028-1041 Liang, J., Song., W. (2012) Clustering based on Steiner points, International Journal of Machine Learning and Cybernetics, DOI: 10.1007/s13042-011-0047-7. Mac Queen, J.B. (1967) Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 2, pp. 281–297. Maharaj, E.A., D’Urso, P. (2011) Fuzzy clustering of time series in the frequency domain, Information Sciences, 2011, 181, 1187-1211. 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 McBratney, A.B., & Moore, A.W. (1985) Application of fuzzy sets to climatic classification. Agricultural and Forest Meteorology, 35, 165–185. Menachemi N, Burke DE, Ayers D, (2004) Factors affecting the adoption of telemedicine – a multiple adopter perspective. J Med Syst 28(6):617–632 Mitchell J, (2000) Increasing the cost-effectiveness of telemedicine by embracing e-health. J Telemed Telecare 6:S16-S19 Nagendran S, Moores D, Spooner R, Triscott J, (2000) Is telemedicine a subset of medical informatics? J Telemed Telecare 6 (Suppl. 2):50–51 Ozkan, I., Turksen, I.B. (2007) Upper and lower values for the level of fuzziness in FCM, Information Sciences, 177, 5143-5152. Pal, N.R., Bezdek, J.C. (1995) On cluster validity for the fuzzy c-means model, IEEE Transactions on Fuzzy Systems, 3, 370-379. Sinova B, Gil MA, Colubi A, Van Aelst S (2012) The median of a random fuzzy number. The 1-norm distance approach, Fuzzy Sets and Systems, in press. Smithson S, Hirschheim R (1998) Analysing information systems evaluation: Another look at an old problem. Eur J Inf Syst 7(3):158-174 Soreson JA, Wang X (1996) ROC methods for evaluation of fMRI techniques, Magn. Res. Med., 36, 737-744. Spagnoletti P, Albano V, Caccetta E, Tarquini R, D’Atri A (2011) “Supporting policy definition in the e-health domain: a QCA based method”, HEALTHINF – International Conference on Health Informatics, 26-29 January, Roma, Italy Stockdale R, Standing C, Love PED, Irani Z (2008) Revisiting the content, context and process of IS evaluation. In: Irani Z and Love PED (eds) Evaluating Information Systems, Public and Private Sector. Butterworth-Heinemann, Oxford, pp 35-45 Wedel M and Kamakura WA (1998) Market segmentation: Conceptual and methodological foundations. Boston: Kluwer Academic. Wilson V (2003) Asynchronous health care communication. Commun ACM 46(6):79-84 Yin RK (2009) Case Study Research: Design and Methods. Fourth Edition. SAGE Publications. California Yusof MM, Kuljis J, Papazafeiropoulou A, Stergioulas LK (2008a) An evaluation framework for health information systems: human, organization and technology-fit factors (HOT-fit). IntJ Med Inform 77(6): 386–398 Yusof MM, Papazafeiropoulou A, Paul RJ, Stergioulas LK (2008b) Investigating evaluation frameworks for health information systems Int J Med Inform 77(6): 377–385 Zadeh LA (2005) Toward a generalized theory of uncertainty (GTU) - An outline. Information Sciences, 172, 1–40.

Item Type:Article
Research documents and activity classification:Journal Articles > Articles > Articles published in or submitted to a Journal without IF refereed / of international relevance
Divisions:Department of Political Science
Department of Business and Management > CeRSI (Information Systems Research Centre)
Uncontrolled Keywords:e-health, healthcare, fuzzy clustering, imprecise evaluation scales, soft taxonomy
MIUR Scientific Area:Area 13 - Economics and Statistics > SECS-P/10 Business Organisation
Area 13 - Economics and Statistics > SECS-S/01 Statistics
DOI:10.1007/s13042-012-0118-4
Deposited By:Paolo Spagnoletti
Deposited On:02 Oct 2012 14:51
Last Modified:02 Oct 2012 14:51

Repository Staff Only: item control page