{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:06:43Z","timestamp":1781712403946,"version":"3.54.5"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:00:00Z","timestamp":1771545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Mol. Biosci."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To estimate the next hemoglobin (Hb) levels in maintenance hemodialysis (MHD) patients, predictive models were developed using various Machine Learning (ML) algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among all the models, the MLP performed the best performance, with an\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      of 0.672, a mean absolute error (MAE) of 9.360 g\/L, and a root mean square error (RMSE) of 12.438 g\/L. The analysis indicated that the most recent Hb value (Hb(t-1)) was the strongest predictor.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmolb.2026.1746108","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T05:21:24Z","timestamp":1771564884000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparison of machine learning models for hemoglobin prediction in patients undergoing maintenance hemodialysis"],"prefix":"10.3389","volume":"13","author":[{"given":"Ting","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Nephrology, The First Afliated Hospital of Jinan University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Nephrology, The First Afliated Hospital of Jinan University","place":["Guangzhou, China"]},{"name":"Department of Nephrology, Dongguan Tungwah Hospital","place":["Dongguan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Yun","sequence":"additional","affiliation":[{"name":"Charit\u00e9 -Universit\u00e4tsmedizin Berlin","place":["Berlin, Germany"]},{"name":"Guangzhou Institute of Technology, Xidian University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Nephrology, The First Afliated Hospital of Jinan University","place":["Guangzhou, China"]},{"name":"Department of Nephrology, The Second Affiliated Hospital of Guangzhou Medical University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuejia","family":"Zheng","sequence":"additional","affiliation":[{"name":"Clinical Medical Research Center, Shenzhen People\u2019s Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University)","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of General Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Materials Science and Engineering, Southern University of Science and Technology","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouping","family":"Zhu","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donge","family":"Tang","sequence":"additional","affiliation":[{"name":"Clinical Medical Research Center, Shenzhen People\u2019s Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University)","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Dai","sequence":"additional","affiliation":[{"name":"Clinical Medical Research Center, Shenzhen People\u2019s Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University)","place":["Shenzhen, China"]},{"name":"The First Affiliated Hospital, School of Medicine, Anhui University of Science and Technology","place":["Huainan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianghong","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Nephrology, The First Afliated Hospital of Jinan University","place":["Guangzhou, China"]},{"name":"Guangzhou Enttxs Medical Products Co., Ltd.","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1186\/s12955-021-01905-7","article-title":"Predictors of poor health-related quality of life among hemodialysis patients with anemia in Jordan","volume":"19","author":"Alshogran","year":"2021","journal-title":"Health Qual. 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