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      Oral pain and comorbidities in an edentulous older population: A k-prototypes cluster analysis

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          Abstract

          Non-odontogenic oral pain is prevalent among the older people and significantly impacts their quality of life. Non-odontogenic oral pain is usually persistent and accompanied by comorbidities such as psychosocial distress and sleep-related problems, which further complicate pain management. The relationship between non-odontogenic oral pain and comorbidities in the older people, however, has not been well documented. This study aimed to identify the factors associated with non-odontogenic oral pain in an edentulous older population and to subgroup this population based on the patterns of oral pain and its associated factors. In this cross-sectional study, data from completely edentulous individuals in the National Health and Nutrition Examination Survey for the period from 2017 to 2020 March (pre-pandemic) were analysed. Associations and correlations between oral pain and 46 other variables, including demographic, questionnaire, examination and laboratory data, were investigated using Pearson’s chi-squared test and Spearman’s rank correlation test. A p value of < 0.05 was considered statistically significant. Clustering of the data was performed using the k-prototypes algorithm, an unsupervised machine learning. Approximately 42% of the edentulous older people experienced oral pain. ‘Having been told to take daily low-dose aspirin’ was significantly associated with oral pain. Oral pain was positively correlated with depressive symptoms and excessive daytime sleepiness (EDS), and negatively correlated with diastolic blood pressure, red blood cell count, haemoglobin level and haematocrit. The k-prototypes algorithm identified a cluster characterised by frequent oral pain, depression and EDS. This study identified distinct patterns of comorbidities among edentulous older people living with oral pain.

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          Most cited references54

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          Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

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            Machine learning in medicine: a practical introduction

            Background Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data. Methods We demonstrate the use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. These algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer Artificial Neural Networks. The publicly-available dataset describing the breast mass samples (N=683) was randomly split into evaluation (n=456) and validation (n=227) samples. We trained algorithms on data from the evaluation sample before they were used to predict the diagnostic outcome in the validation dataset. We compared the predictions made on the validation datasets with the real-world diagnostic decisions to calculate the accuracy, sensitivity, and specificity of the three models. We explored the use of averaging and voting ensembles to improve predictive performance. We provide a step-by-step guide to developing algorithms using the open-source R statistical programming environment. Results The trained algorithms were able to classify cell nuclei with high accuracy (.94 -.96), sensitivity (.97 -.99), and specificity (.85 -.94). Maximum accuracy (.96) and area under the curve (.97) was achieved using the SVM algorithm. Prediction performance increased marginally (accuracy =.97, sensitivity =.99, specificity =.95) when algorithms were arranged into a voting ensemble. Conclusions We use a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be readily applied to other complex tasks including natural language processing and image recognition. Electronic supplementary material The online version of this article (10.1186/s12874-019-0681-4) contains supplementary material, which is available to authorized users.
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              Causal Relationship Between Stressful Life Events and the Onset of Major Depression

              Stressful life events are associated with the onset of episodes of major depression. However, exposure to stressful life events is influenced by genetic factors, and these factors are correlated with those that predispose to major depression. The aim of this study was to clarify the degree to which stressful life events cause major depression.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: ValidationRole: Writing – original draft
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS One
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                13 March 2025
                2025
                : 20
                : 3
                : e0319819
                Affiliations
                [1 ] Institute of Dentistry, Suranaree University of Technology, Nakhon Ratchasima, Thailand
                [2 ] Oral Health Center, Suranaree University of Technology Hospital, Suranaree University of Technology, Nakhon Ratchasima, Thailand
                Far Eastern Memorial Hospital, TAIWAN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-3487-9335
                Article
                PONE-D-24-43284
                10.1371/journal.pone.0319819
                11906073
                40080466
                1459ab12-ba34-472a-9d7c-aeb6ba93b3a1
                © 2025 Chuinsiri, Thinsathid

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 September 2024
                : 9 February 2025
                Page count
                Figures: 5, Tables: 2, Pages: 13
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100004352, Suranaree University of Technology;
                Award Recipient :
                “This work was funded by the Suranaree University of Technology (SUT) Research and Development Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”
                Categories
                Research Article
                Medicine and Health Sciences
                Clinical Medicine
                Signs and Symptoms
                Pain
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Mood Disorders
                Depression
                Medicine and Health Sciences
                Clinical Medicine
                Signs and Symptoms
                Pain
                Neuropathic Pain
                Medicine and Health Sciences
                Hematology
                Biology and Life Sciences
                Physiology
                Physiological Processes
                Sleep
                Engineering and Technology
                Technology Development
                Prototypes
                Medicine and Health Sciences
                Vascular Medicine
                Blood Pressure
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Clustering Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Clustering Algorithms
                Custom metadata
                This study utilised the publicly available anonymised dataset collected from 15,560 non-institutionalised civilian residents of the USA between 2017 and 2020 March (pre-pandemic) as part of the National Health and Nutrition Examination Survey (NHANES). URL: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?Cycle=2017-2020.

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