Evidence map›Paper›PMID 29181232›Full record

ArticleHealthcare informatics research2017

Machine Learning to Compare Frequent Medical Problems of African American and Caucasian Diabetic Kidney Patients.

Yong-Mi Kim, Pranay Kathuria, Dursun Delen

Open access · goldAbstract read
In one paragraph

Article in Healthcare informatics research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 38% of its field
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Yong-Mi KimSchool of Library and Information Studies, University of Oklahoma, Tulsa, OK, USA.
Pranay KathuriaDivision of Nephrology and Hypertension, Department of Medicine, School of Community Medicine, University of Oklahoma, Tulsa, OK, USA.
Dursun DelenSpears School of Business, Oklahoma State University, Tulsa, OK, USA.
Oklahoma State University at Tulsa · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesEnd-stage renal disease (ESRD), which is primarily a consequence of diabetes mellitus, shows an exemplary health disparity between African American and Caucasian patients in the United States. Because diabetic chronic kidney disease (CKD) patients of these two groups show differences in their medical problems, the markers leading to ESRD are also expected to differ. The purpose of this study was, therefore, to compare their medical complications at various levels of kidney function and to identify markers that can be used to predict ESRD.

methodsThe data of type 2 diabetic patients was obtained from the 2012 Cerner database, which totaled 1,038,499 records. The data was then filtered to include only African American and Caucasian outpatients with estimated glomerular filtration rates (eGFR), leaving 4,623 records. A priori machine learning was used to discover frequently appearing medical problems within the filtered data. CKD is defined as abnormalities of kidney structure, present for >3 months.

resultsThis study found that African Americans have much higher rates of CKD-related medical problems than Caucasians for all five stages, and prominent markers leading to ESRD were discovered only for the African American group. These markers are high glucose, high systolic blood pressure (BP), obesity, alcohol/drug use, and low hematocrit. Additionally, the roles of systolic BP and diastolic BP vary depending on the CKD stage.

conclusionsThis research discovered frequently appearing medical problems across five stages of CKD and further showed that many of the markers reported in previous studies are more applicable to African American patients than Caucasian patients.

Indexed as

Electronic Health Records, Renal InsufficiencyGlomerular Filtration RateKidney FailureMachine Learning

Identifiers

PMID29181232
PMCPMC5688022
OpenAlexW2770627596

What OpenQuestion holds

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LicenceCC BY-NC
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