Treatment outcomes and related clinical characteristics in patients with burning mouth syndrome

Oral Diseases ◽  
2020 ◽  
Author(s):  
Moon‐Jong Kim ◽  
Jihoon Kim ◽  
Hong‐Seop Kho
Keyword(s):  
Treatment Outcomes ◽  
Clinical Characteristics ◽  
Burning Mouth Syndrome ◽  
Burning Mouth

Treatment outcomes and related clinical characteristics in patients with burning mouth syndrome

Oral Diseases ◽  
2020 ◽  
Author(s):  
Dmitry Tretiakow ◽  
Katarzyna Lammek ◽  
Andrzej Skorek
Keyword(s):  
Treatment Outcomes ◽  
Clinical Characteristics ◽  
Burning Mouth Syndrome ◽  
Burning Mouth

Clinical characteristics and management outcome in the burning mouth syndrome

1991 ◽  
Vol 72 (2) ◽  
pp. 192-195 ◽  
Author(s):  
Meir Gorsky ◽  
Sol Silverman ◽  
Henry Chinn
Keyword(s):  
Clinical Characteristics ◽  
Burning Mouth Syndrome ◽  
Burning Mouth

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Moon-Jong Kim ◽  
Pil-Jong Kim ◽  
Hong-Gee Kim ◽  
Hong-Seop Kho
Keyword(s):  
Machine Learning ◽  
Treatment Outcomes ◽  
Clinical Data ◽  
Prediction Models ◽  
Burning Mouth Syndrome ◽  
Learning Approach ◽  
Initial Approach ◽  
Machine Learning Approach ◽  
Burning Mouth ◽  
Clonazepam Therapy

AbstractThe 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.

scholarly journals Comparison of the Clinical Characteristics of Patients with Burning Mouth Syndrome due to Hyposalivation

2020 ◽  
Vol 41 (5) ◽  
pp. 838-847
Author(s):  
Ha-nul Lee ◽  
Dong-yoon Kim ◽  
So-young Baek ◽  
Hae In Jeong ◽  
Hyun-Jin Lee ◽  
...  
Keyword(s):  
Clinical Characteristics ◽  
Burning Mouth Syndrome ◽  
Burning Mouth

Saliva flow rates and clinical characteristics of patients with burning mouth syndrome: A case–control study

2021 ◽  
Author(s):  
S. Werfalli ◽  
M. Drangsholt ◽  
J.M. Johnsen ◽  
S.K. Jeffrey ◽  
S. Dakhil ◽  
...  
Keyword(s):  
Clinical Characteristics ◽  
Case Control Study ◽  
Case Control ◽  
Burning Mouth Syndrome ◽  
Flow Rates ◽  
Burning Mouth ◽  
Saliva Flow ◽  
Control Study

scholarly journals Treatment Outcomes of Venlafaxine and Duloxetine in Refractory Burning Mouth Syndrome Patients

2019 ◽  
Vol 44 (3) ◽  
pp. 83-91 ◽  
Author(s):  
Moon-Jong Kim ◽  
Hong-Seop Kho
Keyword(s):  
Treatment Outcomes ◽  
Burning Mouth Syndrome ◽  
Burning Mouth
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