Development of a system to support warfarin dose decisions using deep neural networks

Abstract The first aim of this study was to develop a prothrombin time international normalized ratio (PT INR) prediction model. The second aim was to develop a warfarin maintenance dose decision support system as a precise warfarin dosing platform. Data of 19,719 inpatients from three institutions...

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Autores principales:	Heemoon Lee, Hyun Joo Kim, Hyoung Woo Chang, Dong Jung Kim, Jonghoon Mo, Ji-Eon Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1fef95574618435f816d2a1abd434338
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spelling	oai:doaj.org-article:1fef95574618435f816d2a1abd4343382021-12-02T17:55:13ZDevelopment of a system to support warfarin dose decisions using deep neural networks10.1038/s41598-021-94305-22045-2322https://doaj.org/article/1fef95574618435f816d2a1abd4343382021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94305-2https://doaj.org/toc/2045-2322Abstract The first aim of this study was to develop a prothrombin time international normalized ratio (PT INR) prediction model. The second aim was to develop a warfarin maintenance dose decision support system as a precise warfarin dosing platform. Data of 19,719 inpatients from three institutions was analyzed. The PT INR prediction algorithm included dense and recurrent neural networks, and was designed to predict the 5th-day PT INR from data of days 1–4. Data from patients in one hospital (n = 22,314) was used to train the algorithm which was tested with the datasets from the other two hospitals (n = 12,673). The performance of 5th-day PT INR prediction was compared with 2000 predictions made by 10 expert physicians. A generator of individualized warfarin dose-PT INR tables which simulated the repeated administration of varying doses of warfarin was developed based on the prediction model. The algorithm outperformed humans with accuracy terms of within ± 0.3 of the actual value (machine learning algorithm: 10,650/12,673 cases (84.0%), expert physicians: 1647/2000 cases (81.9%), P = 0.014). In the individualized warfarin dose-PT INR tables generated by the algorithm, the 8th-day PT INR predictions were within 0.3 of actual value in 450/842 cases (53.4%). An artificial intelligence-based warfarin dosing algorithm using a recurrent neural network outperformed expert physicians in predicting future PT INRs. An individualized warfarin dose-PT INR table generator which was constructed based on this algorithm was acceptable.Heemoon LeeHyun Joo KimHyoung Woo ChangDong Jung KimJonghoon MoJi-Eon KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Heemoon Lee Hyun Joo Kim Hyoung Woo Chang Dong Jung Kim Jonghoon Mo Ji-Eon Kim Development of a system to support warfarin dose decisions using deep neural networks
description	Abstract The first aim of this study was to develop a prothrombin time international normalized ratio (PT INR) prediction model. The second aim was to develop a warfarin maintenance dose decision support system as a precise warfarin dosing platform. Data of 19,719 inpatients from three institutions was analyzed. The PT INR prediction algorithm included dense and recurrent neural networks, and was designed to predict the 5th-day PT INR from data of days 1–4. Data from patients in one hospital (n = 22,314) was used to train the algorithm which was tested with the datasets from the other two hospitals (n = 12,673). The performance of 5th-day PT INR prediction was compared with 2000 predictions made by 10 expert physicians. A generator of individualized warfarin dose-PT INR tables which simulated the repeated administration of varying doses of warfarin was developed based on the prediction model. The algorithm outperformed humans with accuracy terms of within ± 0.3 of the actual value (machine learning algorithm: 10,650/12,673 cases (84.0%), expert physicians: 1647/2000 cases (81.9%), P = 0.014). In the individualized warfarin dose-PT INR tables generated by the algorithm, the 8th-day PT INR predictions were within 0.3 of actual value in 450/842 cases (53.4%). An artificial intelligence-based warfarin dosing algorithm using a recurrent neural network outperformed expert physicians in predicting future PT INRs. An individualized warfarin dose-PT INR table generator which was constructed based on this algorithm was acceptable.
format	article
author	Heemoon Lee Hyun Joo Kim Hyoung Woo Chang Dong Jung Kim Jonghoon Mo Ji-Eon Kim
author_facet	Heemoon Lee Hyun Joo Kim Hyoung Woo Chang Dong Jung Kim Jonghoon Mo Ji-Eon Kim
author_sort	Heemoon Lee
title	Development of a system to support warfarin dose decisions using deep neural networks
title_short	Development of a system to support warfarin dose decisions using deep neural networks
title_full	Development of a system to support warfarin dose decisions using deep neural networks
title_fullStr	Development of a system to support warfarin dose decisions using deep neural networks
title_full_unstemmed	Development of a system to support warfarin dose decisions using deep neural networks
title_sort	development of a system to support warfarin dose decisions using deep neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1fef95574618435f816d2a1abd434338
work_keys_str_mv	AT heemoonlee developmentofasystemtosupportwarfarindosedecisionsusingdeepneuralnetworks AT hyunjookim developmentofasystemtosupportwarfarindosedecisionsusingdeepneuralnetworks AT hyoungwoochang developmentofasystemtosupportwarfarindosedecisionsusingdeepneuralnetworks AT dongjungkim developmentofasystemtosupportwarfarindosedecisionsusingdeepneuralnetworks AT jonghoonmo developmentofasystemtosupportwarfarindosedecisionsusingdeepneuralnetworks AT jieonkim developmentofasystemtosupportwarfarindosedecisionsusingdeepneuralnetworks
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Development of a system to support warfarin dose decisions using deep neural networks

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