Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis

The explainable machine learning model developed in this study is a practical tool for healthcare professionals to identify the risk of PEW in patients on MHD and provide decision support for early implementation of personalized nutrition management strategies.

Authors

Institutions

Publication Details

Journal
BMC Nephrology
Published
2025-10-14
DOI
https://doi.org/10.1186/s12882-025-04476-7
Primary Topic
Dialysis and Renal Disease Management
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis

Suhan Zhou, Genlian Cai, Mengyan Pan, Jinping Ying et al.
BMC Nephrology
Dialysis and Renal Disease Management
article

Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis

Suhan Zhou, Genlian Cai, Mengyan Pan, Jinping Ying, Xinxin Xiang, Yujiao Zhang
article en

Abstract

The explainable machine learning model developed in this study is a practical tool for healthcare professionals to identify the risk of PEW in patients on MHD and provide decision support for early implementation of personalized nutrition management strategies.

BMC NephrologyVol. 26(1)
First Affiliated Hospital Zhejiang University (CN), Zhejiang University (CN)
Zhejiang University
Openalex Percentile: Top 70%
Dialysis and Renal Disease Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.