Prediction of dialysis adequacy using data-driven machine learning algorithms

The study results suggest that the development of ML models for accurately predicting dialysis adequacy based on general and intradialytic variables is feasible. These models have the potential to be utilized for noninvasive clinical assessments of dialysis adequacy.

Authors

Juan Chang (ORCID: https://orcid.org/0000-0001-7268-5000)
Rong Li
Yichen Liu (ORCID: https://orcid.org/0000-0002-6011-7628)
J. Meiyan Qing
Lixia Xu

Institutions

Shanghai University of Medicine and Health Sciences (CN)

Publication Details

Journal: Renal Failure
Published: 2024-11-11
DOI: https://doi.org/10.1080/0886022x.2024.2420826
Citations: 1
Primary Topic: Dialysis and Renal Disease Management
Type: article
Field-Weighted Citation Impact: 0.34

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article

Prediction of dialysis adequacy using data-driven machine learning algorithms

Juan Chang, Rong Li, Yichen Liu, J. Meiyan Qing et al.

Nov 11, 2024

1 citations

Renal Failure

Dialysis and Renal Disease Management

0.34

article

Prediction of dialysis adequacy using data-driven machine learning algorithms

Juan Chang, Rong Li, Yichen Liu, J. Meiyan Qing, Lixia Xu

November 11, 2024article en

1 citations

Abstract

Renal FailureVol. 46(2)

Shanghai University of Medicine and Health Sciences (CN)

Reduced inequalities

Openalex Percentile: Top 33%

Dialysis and Renal Disease Management

0.34

DOI Open Access PDF OpenAlex

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