IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v5y2020i2p33-d338530.html
   My bibliography  Save this article

Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

Author

Listed:
  • Sheikh S. Abdullah

    (Insight Lab, Western University, London, ON N6A 3K7, Canada)

  • Neda Rostamzadeh

    (Insight Lab, Western University, London, ON N6A 3K7, Canada)

  • Kamran Sedig

    (Insight Lab, Western University, London, ON N6A 3K7, Canada)

  • Amit X. Garg

    (Department of Medicine, Epidemiology and Biostatistics, Western University, London, ON N6A 3K7, Canada)

  • Eric McArthur

    (ICES, London, ON N6A 3K7, Canada)

Abstract

Medication-induced acute kidney injury (AKI) is a well-known problem in clinical medicine. This paper reports the first development of a visual analytics (VA) system that examines how different medications associate with AKI. In this paper, we introduce and describe VISA_M3R3, a VA system designed to assist healthcare researchers in identifying medications and medication combinations that associate with a higher risk of AKI using electronic medical records (EMRs). By integrating multiple regression models, frequent itemset mining, data visualization, and human-data interaction mechanisms, VISA_M3R3 allows users to explore complex relationships between medications and AKI in such a way that would be difficult or sometimes even impossible without the help of a VA system. Through an analysis of 595 medications using VISA_M3R3, we have identified 55 AKI-inducing medications, 24,212 frequent medication groups, and 78 medication groups that are associated with AKI. The purpose of this paper is to demonstrate the usefulness of VISA_M3R3 in the investigation of medication-induced AKI in particular and other clinical problems in general. Furthermore, this research highlights what needs to be considered in the future when designing VA systems that are intended to support gaining novel and deep insights into massive existing EMRs.

Suggested Citation

  • Sheikh S. Abdullah & Neda Rostamzadeh & Kamran Sedig & Amit X. Garg & Eric McArthur, 2020. "Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3," Data, MDPI, vol. 5(2), pages 1-24, March.
    • Handle: RePEc:gam:jdataj:v:5:y:2020:i:2:p:33-:d:338530
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/5/2/33/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/5/2/33/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Margaret Varga & Caroline Varga, 2016. "Visual Analytics: Data, Analytical and Reasoning Provenance," Springer Proceedings in Business and Economics, in: Victoria L. Lemieux (ed.), Building Trust in Information, pages 141-150, Springer.
    2. McCullagh, Peter, 1984. "Generalized linear models," European Journal of Operational Research, Elsevier, vol. 16(3), pages 285-292, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maede S. Nouri & Daniel J. Lizotte & Kamran Sedig & Sheikh S. Abdullah, 2021. "VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data," Data, MDPI, vol. 6(8), pages 1-19, August.

    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. Tengyuan Liang & Weijie J. Su, 2019. "Statistical inference for the population landscape via moment‐adjusted stochastic gradients," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 431-456, April.
    2. Christian E. Galarza & Panpan Zhang & Víctor H. Lachos, 2021. "Logistic Quantile Regression for Bounded Outcomes Using a Family of Heavy-Tailed Distributions," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 325-349, November.
    3. Anglin, Aaron H. & Wolfe, Marcus T. & Short, Jeremy C. & McKenny, Aaron F. & Pidduck, Robert J., 2018. "Narcissistic rhetoric and crowdfunding performance: A social role theory perspective," Journal of Business Venturing, Elsevier, vol. 33(6), pages 780-812.
    4. Xu Gao & Babak Shahbaba & Hernando Ombao, 2018. "Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 549-579, October.
    5. Kwanda Sydwell Ngwenduna & Rendani Mbuvha, 2021. "Alleviating Class Imbalance in Actuarial Applications Using Generative Adversarial Networks," Risks, MDPI, vol. 9(3), pages 1-33, March.
    6. Jonathan Boss & Alexander Rix & Yin‐Hsiu Chen & Naveen N. Narisetty & Zhenke Wu & Kelly K. Ferguson & Thomas F. McElrath & John D. Meeker & Bhramar Mukherjee, 2021. "A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    7. Monfort, Abel & Villagra, Nuria & Sánchez, Joaquín, 2021. "Economic impact of corporate foundations: An event analysis approach," Journal of Business Research, Elsevier, vol. 122(C), pages 159-170.
    8. Paul Vos, 1995. "Quasi-likelihood or extended quasi-likelihood? An information-geometric approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 47(1), pages 49-64, January.
    9. Trond Petersen, 1986. "Estimating Fully Parametric Hazard Rate Models with Time-Dependent Covariates," Sociological Methods & Research, , vol. 14(3), pages 219-246, February.
    10. Zhengmin Duan & Yonglian Chang & Qi Wang & Tianyao Chen & Qing Zhao, 2018. "A Logistic Regression Based Auto Insurance Rate-Making Model Designed for the Insurance Rate Reform," IJFS, MDPI, vol. 6(1), pages 1-16, February.
    11. Christine Howard & Emma-Liina Marjakangas & Alejandra Morán-Ordóñez & Pietro Milanesi & Aleksandre Abuladze & Karen Aghababyan & Vitalie Ajder & Volen Arkumarev & Dawn E. Balmer & Hans-Günther Bauer &, 2023. "Local colonisations and extinctions of European birds are poorly explained by changes in climate suitability," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    12. William H. Greene & David A. Hensher, 2008. "Modeling Ordered Choices: A Primer and Recent Developments," Working Papers 08-26, New York University, Leonard N. Stern School of Business, Department of Economics.
    13. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    14. Daniel Kouloukoui & Sônia Maria da Silva Gomes & Marcia Mara de Oliveira Marinho & Ednildo Andrade Torres & Asher Kiperstok & Pieter de Jong, 2018. "Disclosure of climate risk information by the world’s largest companies," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(8), pages 1251-1279, December.
    15. Yuanqiao Li & Xiuchen Wu & Yongmei Huang & Xiaoyan Li & Fangzhong Shi & Shoudong Zhao & Yuting Yang & Yuhong Tian & Pei Wang & Shulei Zhang & Cicheng Zhang & Yang Wang & Chongyang Xu & Pengwu Zhao, 2021. "Compensation effect of winter snow on larch growth in Northeast China," Climatic Change, Springer, vol. 164(3), pages 1-17, February.
    16. Warnick, Benjamin J. & Davis, Blakley C. & Allison, Thomas H. & Anglin, Aaron H., 2021. "Express yourself: Facial expression of happiness, anger, fear, and sadness in funding pitches," Journal of Business Venturing, Elsevier, vol. 36(4).
    17. Anglin, Aaron H. & Short, Jeremy C. & Drover, Will & Stevenson, Regan M. & McKenny, Aaron F. & Allison, Thomas H., 2018. "The power of positivity? The influence of positive psychological capital language on crowdfunding performance," Journal of Business Venturing, Elsevier, vol. 33(4), pages 470-492.
    18. Tajvarpour, Mohammad Hossein & Pujari, Devashish, 2022. "The influence of narrative description on the success of crowdfunding campaigns: The moderating role of quality signals," Journal of Business Research, Elsevier, vol. 149(C), pages 123-138.
    19. Yang, Jing Yu & Wen, Liang & Volk, Stefan & Lu, Jane Wenzhen, 2022. "Temporal boundaries and expatriate staffing: Effects of parent–subsidiary work-time overlap," Journal of World Business, Elsevier, vol. 57(6).
    20. Rafael J. Buralli & Helena Ribeiro & Thais Mauad & Luís F. Amato-Lourenço & João M. Salge & Fredi A. Diaz-Quijano & Renata S. Leão & Rejane C. Marques & Daniele S. Silva & Jean Remy Davée Guimarães, 2018. "Respiratory Condition of Family Farmers Exposed to Pesticides in the State of Rio de Janeiro, Brazil," IJERPH, MDPI, vol. 15(6), pages 1-14, June.

    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:gam:jdataj:v:5:y:2020:i:2:p:33-:d:338530. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.