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Reading: Extracting Deep Phenotypes for Chronic Kidney Disease Using Electronic Health Records

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Case study

Extracting Deep Phenotypes for Chronic Kidney Disease Using Electronic Health Records

Authors:

Duc Thanh Anh Luong ,

University at Buffalo
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Dinh Tran,

University at Buffalo
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Wilson D. Pace,

University of Colorado, Denver
About Wilson D.
MD, FAAFP
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Miriam Dickinson,

University of Colorado, Denver
About Miriam
PhD
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Joseph Vassalotti,

Icahn School of Medicine at Mount Sinai
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MD
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Jennifer Carroll,

University of Colorado, Denver
About Jennifer
MD, MPH
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Matthew Withiam-Leitch,

University at Buffalo
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MD
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Min Yang,

University at Buffalo
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MD, PhD
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Nikhil Satchidanand,

University at Buffalo
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PhD
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Elizabeth Staton,

University of Colorado, Denver
About Elizabeth
MSTC
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Linda S. Kahn,

University at Buffalo
About Linda S.
PhD
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Varun Chandola,

University at Buffalo
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PhD
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Chester H. Fox

University at Buffalo
About Chester H.
MD
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Abstract

Introduction: As chronic kidney disease (CKD) is among the most prevalent chronic diseases in the world with various rate of progression among patients, identifying its phenotypic subtypes is important for improving risk stratification and providing more targeted therapy and specific treatments for patients having different trajectories of the disease progression.

Problem Definition and Data: The rapid growth and adoption of electronic health records (EHR) technology has created a unique opportunity to leverage the abundant clinical data, available as EHRs, to find meaningful phenotypic subtypes for CKD. In this study, we focus on extracting disease severity profiles for CKD while accounting for other confounding factors.

Probabilistic Subtyping Model: We employ a probabilistic model to identify precise phenotypes from EHR data of patients who have chronic kidney disease. Using this model, patient’s eGFR trajectory is decomposed as a combination of four different components including disease subtype effect, covariate effect, individual long-term effect and individual short-term effect.

Experimental Results: The discovered disease subtypes obtained by Probabilistic Subtyping Model for CKD are presented and their clinical relevance is analyzed.

Discussion: Several clinical health markers that were found associated with disease subtypes are presented with suggestion for further investigation on their use as risk predictors. Several assumptions in the study are also clarified and discussed.

How to Cite: Luong DTA, Tran D, Pace WD, Dickinson M, Vassalotti J, Carroll J, et al.. Extracting Deep Phenotypes for Chronic Kidney Disease Using Electronic Health Records. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2017;5(1):9. DOI: http://doi.org/10.13063/egems.1259
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Published on 12 Jun 2017.
Peer Reviewed

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