IDEAS home Printed from https://ideas.repec.org/a/igg/jhisi0/v16y2021i4p1-25.html
   My bibliography  Save this article

A Data Representation Model for Personalized Medicine

Author

Listed:
  • Hafid Kadi

    (Department of Computer Science, University of Mustapha Stambouli, Mascara, Algeria & Normandie University, UNICAEN, ENSICAEN, CNRS, GREYC, Caen, France)

  • Mohammed Rebbah

    (Department of Computer Science, University of Mustapha Stambouli, Mascara, Algeria)

  • Boudjelal Meftah

    (Department of Computer Science, University of Mustapha Stambouli, Mascara, Algeria)

  • Olivier Lézoray

    (Normandie University, UNICAEN, ENSICAEN, CNRS, GREYC, Caen, France)

Abstract

Personalized medicine exploits the patient data, for example, genetic compositions, and key biomarkers. During the data mining process, the key challenges are the information loss, the data types heterogeneity and the time series representation. In this paper, a novel data representation model for personalized medicine is proposed in light of these challenges. The proposed model will account for the structured, temporal and non-temporal data and their types, namely, numeric, nominal, date, and Boolean. After the "Date and Boolean" data transformation, the nominal data are treated by dispersion while several clustering techniques are deployed to control the numeric data distribution. Ultimately, the transformation process results in three homogeneous representations with these representations having only two dimensions to ease the exploration of the represented dataset. Compared to the Symbolic Aggregate Approximation technique, the proposed model preserves the time-series information, conserves as much data as possible and offers multiple simple representations to be explored.

Suggested Citation

  • Hafid Kadi & Mohammed Rebbah & Boudjelal Meftah & Olivier Lézoray, 2021. "A Data Representation Model for Personalized Medicine," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(4), pages 1-25, October.
    • Handle: RePEc:igg:jhisi0:v:16:y:2021:i:4:p:1-25
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJHISI.295822
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. J. A. Hartigan & M. A. Wong, 1979. "A K‐Means Clustering Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 100-108, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Weibin & Zha, Huazhu & Zhang, Shuai & Ma, Lei, 2023. "Road section traffic flow prediction method based on the traffic factor state network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    2. Jelle R Dalenberg & Luca Nanetti & Remco J Renken & René A de Wijk & Gert J ter Horst, 2014. "Dealing with Consumer Differences in Liking during Repeated Exposure to Food; Typical Dynamics in Rating Behavior," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    3. Custodio João, Igor & Lucas, André & Schaumburg, Julia & Schwaab, Bernd, 2023. "Dynamic clustering of multivariate panel data," Journal of Econometrics, Elsevier, vol. 237(2).
    4. Utkarsh J. Dang & Michael P.B. Gallaugher & Ryan P. Browne & Paul D. McNicholas, 2023. "Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 145-167, April.
    5. Bernd Scherer & Diogo Judice & Stephan Kessler, 2010. "Price reversals in global equity markets," Journal of Asset Management, Palgrave Macmillan, vol. 11(5), pages 332-345, December.
    6. Ugofilippo Basellini & Carlo Giovanni Camarda, 2020. "Modelling COVID-19 mortality at the regional level in Italy," Working Papers axq0sudakgkzhr-blecv, French Institute for Demographic Studies.
    7. Jing Xiao & Qiongqiong Xu & Chuanli Wu & Yuexia Gao & Tianqi Hua & Chenwu Xu, 2016. "Performance Evaluation of Missing-Value Imputation Clustering Based on a Multivariate Gaussian Mixture Model," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-14, August.
    8. Humberto Elias Garcia Lopes & Marlusa de Sevilha Gosling, 2021. "Cluster Analysis in Practice: Dealing with Outliers in Managerial Research," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 25(1), pages 200081-2000.
    9. Mónika-Anetta Alt & Vizeli Ibolya, 2021. "Identifying Relevant Segments of Potential Banking Chatbot Users Based on Technology Adoption Behavior," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 33(2), pages 165-183.
    10. Naderi, Mehrdad & Hung, Wen-Liang & Lin, Tsung-I & Jamalizadeh, Ahad, 2019. "A novel mixture model using the multivariate normal mean–variance mixture of Birnbaum–Saunders distributions and its application to extrasolar planets," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 126-138.
    11. Alan Fernihough & Kevin Hjortshøj, 2021. "Coal and the European Industrial Revolution," The Economic Journal, Royal Economic Society, vol. 131(635), pages 1135-1149.
    12. Mohiuddin, Hossain & Fitch-Polse, Dillon T. & Handy, Susan L., 2024. "Examining market segmentation to increase bike-share use and enhance equity: The case of the greater Sacramento region," Transport Policy, Elsevier, vol. 145(C), pages 279-290.
    13. Roberto Benocci & Giovanni Brambilla & Alessandro Bisceglie & Giovanni Zambon, 2020. "Eco-Acoustic Indices to Evaluate Soundscape Degradation Due to Human Intrusion," Sustainability, MDPI, vol. 12(24), pages 1-19, December.
    14. Emma L. Schultz & David T. Tan & Kathleen D. Walsh, 2010. "Endogeneity and the corporate governance - performance relation," Australian Journal of Management, Australian School of Business, vol. 35(2), pages 145-163, August.
    15. Ting Liu & Nick Shryane & Mark Elliot, 2022. "Attitudes to climate change risk: classification of and transitions in the UK population between 2012 and 2020," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-15, December.
    16. Renata De Paris & Christian V Quevedo & Duncan D A Ruiz & Osmar Norberto de Souza, 2015. "An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-25, July.
    17. Stefania Salvatore & Diana Domanska & Mollie Wood & Hedvig Nordeng & Geir Kjetil Sandve, 2017. "Complex patterns of concomitant medication use: A study among Norwegian women using paracetamol during pregnancy," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-11, December.
    18. F. Murtagh & M. Hernández-Pajares, 1995. "The Kohonen self-organizing map method: An assessment," Journal of Classification, Springer;The Classification Society, vol. 12(2), pages 165-190, September.
    19. Köchling, Anne, 2022. "Zwischen Reisemotivation und Reisebarrieren: Analyse von Urlaubseinstellungstypen in neun westlichen Ländern nach fast zwei Jahren Corona-Pandemie," Working Paper Series 2, Deutsches Institut für Tourismusforschung, Fachhochschule Westküste.
    20. Senfeng Cen & Jae Hung Yoo & Chang Gyoon Lim, 2022. "Electricity Pattern Analysis by Clustering Domestic Load Profiles Using Discrete Wavelet Transform," Energies, MDPI, vol. 15(4), pages 1-18, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jhisi0:v:16:y:2021:i:4:p:1-25. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.