Dialysis adequacy predictions using a machine learning method

Abstract Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However, there are inconveniences and disadvantages to measuring dialysis adequacy by blood samples. This study used machine learning models to predict dialysis adequacy in chronic hemodialysis patie...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hyung Woo Kim, Seok-Jae Heo, Jae Young Kim, Annie Kim, Chung-Mo Nam, Beom Seok Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7e25d2b59aef40c79b19d54c65c0fef6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7e25d2b59aef40c79b19d54c65c0fef6
record_format	dspace
spelling	oai:doaj.org-article:7e25d2b59aef40c79b19d54c65c0fef62021-12-02T16:31:03ZDialysis adequacy predictions using a machine learning method10.1038/s41598-021-94964-12045-2322https://doaj.org/article/7e25d2b59aef40c79b19d54c65c0fef62021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94964-1https://doaj.org/toc/2045-2322Abstract Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However, there are inconveniences and disadvantages to measuring dialysis adequacy by blood samples. This study used machine learning models to predict dialysis adequacy in chronic hemodialysis patients using repeatedly measured data during hemodialysis. This study included 1333 hemodialysis sessions corresponding to the monthly examination dates of 61 patients. Patient demographics and clinical parameters were continuously measured from the hemodialysis machine; 240 measurements were collected from each hemodialysis session. Machine learning models (random forest and extreme gradient boosting [XGBoost]) and deep learning models (convolutional neural network and gated recurrent unit) were compared with multivariable linear regression models. The mean absolute percentage error (MAPE), root mean square error (RMSE), and Spearman’s rank correlation coefficient (Corr) for each model using fivefold cross-validation were calculated as performance measurements. The XGBoost model had the best performance among all methods (MAPE = 2.500; RMSE = 2.906; Corr = 0.873). The deep learning models with convolutional neural network (MAPE = 2.835; RMSE = 3.125; Corr = 0.833) and gated recurrent unit (MAPE = 2.974; RMSE = 3.230; Corr = 0.824) had similar performances. The linear regression models had the lowest performance (MAPE = 3.284; RMSE = 3.586; Corr = 0.770) compared with other models. Machine learning methods can accurately infer hemodialysis adequacy using continuously measured data from hemodialysis machines.Hyung Woo KimSeok-Jae HeoJae Young KimAnnie KimChung-Mo NamBeom Seok KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hyung Woo Kim Seok-Jae Heo Jae Young Kim Annie Kim Chung-Mo Nam Beom Seok Kim Dialysis adequacy predictions using a machine learning method
description	Abstract Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However, there are inconveniences and disadvantages to measuring dialysis adequacy by blood samples. This study used machine learning models to predict dialysis adequacy in chronic hemodialysis patients using repeatedly measured data during hemodialysis. This study included 1333 hemodialysis sessions corresponding to the monthly examination dates of 61 patients. Patient demographics and clinical parameters were continuously measured from the hemodialysis machine; 240 measurements were collected from each hemodialysis session. Machine learning models (random forest and extreme gradient boosting [XGBoost]) and deep learning models (convolutional neural network and gated recurrent unit) were compared with multivariable linear regression models. The mean absolute percentage error (MAPE), root mean square error (RMSE), and Spearman’s rank correlation coefficient (Corr) for each model using fivefold cross-validation were calculated as performance measurements. The XGBoost model had the best performance among all methods (MAPE = 2.500; RMSE = 2.906; Corr = 0.873). The deep learning models with convolutional neural network (MAPE = 2.835; RMSE = 3.125; Corr = 0.833) and gated recurrent unit (MAPE = 2.974; RMSE = 3.230; Corr = 0.824) had similar performances. The linear regression models had the lowest performance (MAPE = 3.284; RMSE = 3.586; Corr = 0.770) compared with other models. Machine learning methods can accurately infer hemodialysis adequacy using continuously measured data from hemodialysis machines.
format	article
author	Hyung Woo Kim Seok-Jae Heo Jae Young Kim Annie Kim Chung-Mo Nam Beom Seok Kim
author_facet	Hyung Woo Kim Seok-Jae Heo Jae Young Kim Annie Kim Chung-Mo Nam Beom Seok Kim
author_sort	Hyung Woo Kim
title	Dialysis adequacy predictions using a machine learning method
title_short	Dialysis adequacy predictions using a machine learning method
title_full	Dialysis adequacy predictions using a machine learning method
title_fullStr	Dialysis adequacy predictions using a machine learning method
title_full_unstemmed	Dialysis adequacy predictions using a machine learning method
title_sort	dialysis adequacy predictions using a machine learning method
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7e25d2b59aef40c79b19d54c65c0fef6
work_keys_str_mv	AT hyungwookim dialysisadequacypredictionsusingamachinelearningmethod AT seokjaeheo dialysisadequacypredictionsusingamachinelearningmethod AT jaeyoungkim dialysisadequacypredictionsusingamachinelearningmethod AT anniekim dialysisadequacypredictionsusingamachinelearningmethod AT chungmonam dialysisadequacypredictionsusingamachinelearningmethod AT beomseokkim dialysisadequacypredictionsusingamachinelearningmethod
_version_	1718383884089950208

Dialysis adequacy predictions using a machine learning method

Ejemplares similares