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

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  • المؤلفون: Kim MJ;Kim MJ; Kim PJ; Kim PJ; Kim HG; Kim HG; Kho HS; Kho HS; Kho HS
  • المصدر:
    Scientific reports [Sci Rep] 2021 Jul 28; Vol. 11 (1), pp. 15396. Date of Electronic Publication: 2021 Jul 28.
  • نوع النشر :
    Journal Article; Research Support, Non-U.S. Gov't
  • اللغة:
    English
  • معلومة اضافية
    • المصدر:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • الموضوع:
      Machine Learning* ; Prognosis*; Burning Mouth Syndrome/*therapy ; Clonazepam/*therapeutic use; Burning Mouth Syndrome/diagnosis ; Burning Mouth Syndrome/pathology ; Clonazepam/adverse effects ; Female ; Humans ; Lubricants/adverse effects ; Lubricants/therapeutic use ; Male ; Middle Aged ; Mucositis/drug therapy ; Mucositis/pathology ; Treatment Outcome
    • نبذة مختصرة :
      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.
      (© 2021. The Author(s).)
    • References:
      Int J Oral Maxillofac Surg. 2018 Jul;47(7):879-887. (PMID: 29506949)
      Pain. 2015 Feb;156(2):356. (PMID: 25599458)
      Laryngoscope. 2012 Apr;122(4):813-6. (PMID: 22344742)
      Pain. 2010 Apr;149(1):27-32. (PMID: 20083352)
      Oral Dis. 2016 Sep;22(6):503-11. (PMID: 26680638)
      Arch Gerontol Geriatr. 2010 Jul-Aug;51(1):95-9. (PMID: 19775760)
      Oral Dis. 2020 Oct 24;:. (PMID: 33098162)
      Pain. 2004 Mar;108(1-2):51-7. (PMID: 15109507)
      Oral Maxillofac Surg Clin North Am. 2016 Aug;28(3):381-96. (PMID: 27475513)
      Arch Gerontol Geriatr. 2011 Nov-Dec;53(3):263-9. (PMID: 21075463)
      Arch Gerontol Geriatr. 2012 Nov-Dec;55(3):755-61. (PMID: 22040716)
      Pain. 2018 Mar;159(3):610-613. (PMID: 29257770)
      Int J Oral Maxillofac Surg. 2020 Jan;49(1):38-43. (PMID: 31235389)
      J Orofac Pain. 1998 Fall;12(4):272-8. (PMID: 10425973)
      Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065):20150202. (PMID: 26953178)
    • الرقم المعرف:
      0 (Lubricants)
      5PE9FDE8GB (Clonazepam)
    • الموضوع:
      Date Created: 20210729 Date Completed: 20211101 Latest Revision: 20211101
    • الموضوع:
      20240829
    • الرقم المعرف:
      PMC8319111
    • الرقم المعرف:
      10.1038/s41598-021-94940-9
    • الرقم المعرف:
      34321575