Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data

Abstract The purpose of this study is to apply a machine learning approach to predict whether patients with burning mouth syndrome (BMS) respond to the initial approach and clonazepam therapy based on clinical data. Among the patients with the primary type of BMS who visited the clinic from 2006 to...

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Autores principales:	Moon-Jong Kim, Pil-Jong Kim, Hong-Gee Kim, Hong-Seop Kho
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6e44f0097a6f409cab7ce84b00053808
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spelling	oai:doaj.org-article:6e44f0097a6f409cab7ce84b000538082021-12-02T18:47:08ZPrediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data10.1038/s41598-021-94940-92045-2322https://doaj.org/article/6e44f0097a6f409cab7ce84b000538082021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94940-9https://doaj.org/toc/2045-2322Abstract The purpose of this study is to apply a machine learning approach to predict whether patients with burning mouth syndrome (BMS) respond to the initial approach and clonazepam therapy based on clinical data. Among the patients with the primary type of BMS who visited the clinic from 2006 to 2015, those treated with the initial approach of detailed explanation regarding home care instruction and use of oral topical lubricants, or who were prescribed clonazepam for a minimum of 1 month were included in this study. The clinical data and treatment outcomes were collected from medical records. Extreme Gradient-Boosted Decision Trees was used for machine learning algorithms to construct prediction models. Accuracy of the prediction models was evaluated and feature importance calculated. The accuracy of the prediction models for the initial approach and clonazepam therapy was 67.6% and 67.4%, respectively. Aggravating factors and psychological distress were important features in the prediction model for the initial approach, and intensity of symptoms before administration was the important feature in the prediction model for clonazepam therapy. In conclusion, the analysis of treatment outcomes in patients with BMS using a machine learning approach showed meaningful results of clinical applicability.Moon-Jong KimPil-Jong KimHong-Gee KimHong-Seop KhoNature 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 Moon-Jong Kim Pil-Jong Kim Hong-Gee Kim Hong-Seop Kho Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data
description	Abstract The purpose of this study is to apply a machine learning approach to predict whether patients with burning mouth syndrome (BMS) respond to the initial approach and clonazepam therapy based on clinical data. Among the patients with the primary type of BMS who visited the clinic from 2006 to 2015, those treated with the initial approach of detailed explanation regarding home care instruction and use of oral topical lubricants, or who were prescribed clonazepam for a minimum of 1 month were included in this study. The clinical data and treatment outcomes were collected from medical records. Extreme Gradient-Boosted Decision Trees was used for machine learning algorithms to construct prediction models. Accuracy of the prediction models was evaluated and feature importance calculated. The accuracy of the prediction models for the initial approach and clonazepam therapy was 67.6% and 67.4%, respectively. Aggravating factors and psychological distress were important features in the prediction model for the initial approach, and intensity of symptoms before administration was the important feature in the prediction model for clonazepam therapy. In conclusion, the analysis of treatment outcomes in patients with BMS using a machine learning approach showed meaningful results of clinical applicability.
format	article
author	Moon-Jong Kim Pil-Jong Kim Hong-Gee Kim Hong-Seop Kho
author_facet	Moon-Jong Kim Pil-Jong Kim Hong-Gee Kim Hong-Seop Kho
author_sort	Moon-Jong Kim
title	Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data
title_short	Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data
title_full	Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data
title_fullStr	Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data
title_full_unstemmed	Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data
title_sort	prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6e44f0097a6f409cab7ce84b00053808
work_keys_str_mv	AT moonjongkim predictionoftreatmentoutcomeinburningmouthsyndromepatientsusingmachinelearningbasedonclinicaldata AT piljongkim predictionoftreatmentoutcomeinburningmouthsyndromepatientsusingmachinelearningbasedonclinicaldata AT honggeekim predictionoftreatmentoutcomeinburningmouthsyndromepatientsusingmachinelearningbasedonclinicaldata AT hongseopkho predictionoftreatmentoutcomeinburningmouthsyndromepatientsusingmachinelearningbasedonclinicaldata
_version_	1718377685392031744

Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data

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