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Toward Personalized Treatment of Chronic Diseases: The CKDCase Study

Published: 23 October 2017 Publication History

Abstract

Chronic diseases greatly influence the patients' life and incur the bulk of healthcare costs. Medical treatments should be personalized to consider individual variance. In this study, we take a first step toward personalized treatment of chronic kidney disease by formulating two prediction problems. We utilize random forest to learn the prediction models, and the preliminary results look promising.

References

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2012. KDIGO Clinical Practice Guideline for the Evaluation and Management of CKD. http://www.ajkd.org/article/S0272-6386(14)00491-0/fulltext. (2012).
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Thomas Bodenheimer, Ellen Chen, and Heather D. Bennett. 2009. Confronting the growing burden of chronic disease: can the US health care workforce do the job? Health Affairs, Vol. 28, 1 (2009), 64--74.
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Leo Breiman. 2001. Random forests. Machine learning, Vol. 45, 1 (2001), 5--32.
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Corinna Cortes and Mehryar Mohri. 2004. AUC optimization vs. error rate minimization. In Advances in neural information processing systems. 313--320.
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National Kidney Foundation. 2002. Clinical practice guidelines for chronic kidney disease: evaluation, classification and stratification. National Kidney Foundation.
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Abhijit V. Kshirsagar, Heejung Bang, Andrew S. Bomback, Suma Vupputuri, David A. Shoham, Lisa M. Kern, Philip J. Klemmer, Madhu Mazumdar, and Phyllis A. August. 2008. A simple algorithm to predict incident kidney disease. Archives of internal medicine Vol. 168, 22 (2008), 2466--2473.
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Gilles Louppe, Louis Wehenkel, Antonio Sutera, and Pierre Geurts. 2013. Understanding variable importances in forests of randomized trees Advances in neural information processing systems. 431--439.
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Hani Neuvirth, Michal Ozery-Flato, Jianying Hu, Jonathan Laserson, Martin S. Kohn, Shahram Ebadollahi, and Michal Rosen-Zvi. 2011. Toward personalized care management of patients at risk: the diabetes case study. (2011), 395--403.
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Nicholas J. Schork. 2015. Personalized medicine: time for one-person trials. Nature, Vol. 520, 7549 (2015), 609--11.
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  • (2023)Chronic Care in a Life Transition: Challenges and Opportunities for Artificial Intelligence to Support Young Adults With Type 1 Diabetes Moving to UniversityProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580901(1-16)Online publication date: 19-Apr-2023

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    cover image ACM Conferences
    MMHealth '17: Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care
    October 2017
    104 pages
    ISBN:9781450355049
    DOI:10.1145/3132635
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 23 October 2017

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    Author Tags

    1. chronic kidney disease
    2. personalized medicine

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    • Short-paper

    Funding Sources

    • Chang Gung Memorial Hospital Research Projects

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    MM '17
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    MM '17: ACM Multimedia Conference
    October 23, 2017
    California, Mountain View, USA

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    • (2023)Chronic Care in a Life Transition: Challenges and Opportunities for Artificial Intelligence to Support Young Adults With Type 1 Diabetes Moving to UniversityProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580901(1-16)Online publication date: 19-Apr-2023

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