Evidence map›Paper›PMID 41797993›Full record

ArticleFrontiers in molecular biosciences2026

Comparison of machine learning models for hemoglobin prediction in patients undergoing maintenance hemodialysis.

Ting Xie, Xiaoyan Su, Chen Yun, Xiaohong Tang, Xuejia Zheng, Jingjing Dong, Qi Guo, Shouping Zhu, Donge Tang, Yong Dai and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in molecular biosciences, 2026. 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
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Ting Xie *Department of Nephrology, The First Afliated Hospital of Jinan University, Guangzhou, China.
Xiaoyan Su *Department of Nephrology, The First Afliated Hospital of Jinan University, Guangzhou, China.
Chen Yun *Charité -Universitätsmedizin Berlin, Berlin, Germany.
Xiaohong TangDepartment of Nephrology, The First Afliated Hospital of Jinan University, Guangzhou, China.
Xuejia ZhengClinical Medical Research Center, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University), Shenzhen, China.
Jingjing DongDepartment of General Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
Qi GuoDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
Shouping ZhuGuangzhou Institute of Technology, Xidian University, Guangzhou, China.
Donge TangClinical Medical Research Center, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University), Shenzhen, China.
Yong DaiClinical Medical Research Center, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University), Shenzhen, China.
Lianghong YinDepartment of Nephrology, The First Afliated Hospital of Jinan University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To estimate the next hemoglobin (Hb) levels in maintenance hemodialysis (MHD) patients, predictive models were developed using various Machine Learning (ML) algorithms. Methods: A total of 8,159 records from 2,104 MHD patients across 24 blood purification centers in Shenzhen were included. Eight ML algorithms were employed to develop prediction models: Linear Regression (LR), Least Absolute Shrinkage and Selection Operator (Lasso), Bayesian Ridge, Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM). Subsequently, the performance of models was evaluated and compared. Results: Among all the models, the MLP performed the best performance, with an Conclusion: ML models based on demographic characteristics, dialysis records, and historical Hb data can effectively predict future Hb levels in MHD patients, which is helpful for early identification of anemia risk and timely clinical intervention.

Indexed as

chronic kidney diseasehemoglobinmachine learningmaintenance hemodialysismultilayer perceptron

Identifiers

PMID41797993
PMCPMC12962952

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.