Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling

Abstract Patients requiring low-dose warfarin are more likely to suffer bleeding due to overdose. The goal of this work is to improve the feedforward neural network model's precision in predicting the low maintenance dose for Chinese in the aspect of training data construction. We built the mod...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Weijie Ma, Hongying Li, Li Dong, Qin Zhou, Bo Fu, Jiang-long Hou, Jing Wang, Wenzhe Qin, Jin Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5a52681b850b4718b18e30a28324d339
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5a52681b850b4718b18e30a28324d339
record_format	dspace
spelling	oai:doaj.org-article:5a52681b850b4718b18e30a28324d3392021-12-02T16:31:57ZWarfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling10.1038/s41598-021-93317-22045-2322https://doaj.org/article/5a52681b850b4718b18e30a28324d3392021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93317-2https://doaj.org/toc/2045-2322Abstract Patients requiring low-dose warfarin are more likely to suffer bleeding due to overdose. The goal of this work is to improve the feedforward neural network model's precision in predicting the low maintenance dose for Chinese in the aspect of training data construction. We built the model from a resampled dataset created by equal stratified sampling (maintaining the same sample number in three dose-groups with a total of 3639) and performed internal and external validations. Comparing to the model trained from the raw dataset of 19,060 eligible cases, we improved the low-dose group's ideal prediction percentage from 0.7 to 9.6% and maintained the overall performance (76.4% vs. 75.6%) in external validation. We further built neural network models on single-dose subsets to invest whether the subsets samples were sufficient and whether the selected factors were appropriate. The training set sizes were 1340 and 1478 for the low and high dose subsets; the corresponding ideal prediction percentages were 70.2% and 75.1%. The training set size for the intermediate dose varied and was 1553, 6214, and 12,429; the corresponding ideal prediction percentages were 95.6, 95.1%, and 95.3%. Our conclusion is that equal stratified sampling can be a considerable alternative approach in training data construction to build drug dosing models in the clinic.Weijie MaHongying LiLi DongQin ZhouBo FuJiang-long HouJing WangWenzhe QinJin ChenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Weijie Ma Hongying Li Li Dong Qin Zhou Bo Fu Jiang-long Hou Jing Wang Wenzhe Qin Jin Chen Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling
description	Abstract Patients requiring low-dose warfarin are more likely to suffer bleeding due to overdose. The goal of this work is to improve the feedforward neural network model's precision in predicting the low maintenance dose for Chinese in the aspect of training data construction. We built the model from a resampled dataset created by equal stratified sampling (maintaining the same sample number in three dose-groups with a total of 3639) and performed internal and external validations. Comparing to the model trained from the raw dataset of 19,060 eligible cases, we improved the low-dose group's ideal prediction percentage from 0.7 to 9.6% and maintained the overall performance (76.4% vs. 75.6%) in external validation. We further built neural network models on single-dose subsets to invest whether the subsets samples were sufficient and whether the selected factors were appropriate. The training set sizes were 1340 and 1478 for the low and high dose subsets; the corresponding ideal prediction percentages were 70.2% and 75.1%. The training set size for the intermediate dose varied and was 1553, 6214, and 12,429; the corresponding ideal prediction percentages were 95.6, 95.1%, and 95.3%. Our conclusion is that equal stratified sampling can be a considerable alternative approach in training data construction to build drug dosing models in the clinic.
format	article
author	Weijie Ma Hongying Li Li Dong Qin Zhou Bo Fu Jiang-long Hou Jing Wang Wenzhe Qin Jin Chen
author_facet	Weijie Ma Hongying Li Li Dong Qin Zhou Bo Fu Jiang-long Hou Jing Wang Wenzhe Qin Jin Chen
author_sort	Weijie Ma
title	Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling
title_short	Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling
title_full	Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling
title_fullStr	Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling
title_full_unstemmed	Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling
title_sort	warfarin maintenance dose prediction for chinese after heart valve replacement by a feedforward neural network with equal stratified sampling
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5a52681b850b4718b18e30a28324d339
work_keys_str_mv	AT weijiema warfarinmaintenancedosepredictionforchineseafterheartvalvereplacementbyafeedforwardneuralnetworkwithequalstratifiedsampling AT hongyingli warfarinmaintenancedosepredictionforchineseafterheartvalvereplacementbyafeedforwardneuralnetworkwithequalstratifiedsampling AT lidong warfarinmaintenancedosepredictionforchineseafterheartvalvereplacementbyafeedforwardneuralnetworkwithequalstratifiedsampling AT qinzhou warfarinmaintenancedosepredictionforchineseafterheartvalvereplacementbyafeedforwardneuralnetworkwithequalstratifiedsampling AT bofu warfarinmaintenancedosepredictionforchineseafterheartvalvereplacementbyafeedforwardneuralnetworkwithequalstratifiedsampling AT jianglonghou warfarinmaintenancedosepredictionforchineseafterheartvalvereplacementbyafeedforwardneuralnetworkwithequalstratifiedsampling AT jingwang warfarinmaintenancedosepredictionforchineseafterheartvalvereplacementbyafeedforwardneuralnetworkwithequalstratifiedsampling AT wenzheqin warfarinmaintenancedosepredictionforchineseafterheartvalvereplacementbyafeedforwardneuralnetworkwithequalstratifiedsampling AT jinchen warfarinmaintenancedosepredictionforchineseafterheartvalvereplacementbyafeedforwardneuralnetworkwithequalstratifiedsampling
_version_	1718383810237693952

Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling

Ejemplares similares