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Development of Risk Prediction Models for Severe Periodontitis in a Thai Population: Statistical and Machine Learning Approaches.
JMIR Form Res
December 2023
Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Article Synopsis
- Severe periodontitis affects a significant portion of Thai adults (26%) and globally (11.2%), causing loss of alveolar bone, highlighting the need for effective screening methods due to the resource-intensive nature of traditional diagnostic techniques.
- This study compares risk prediction models using both traditional statistical methods (like logistic regression) and modern machine learning techniques to identify those at high risk for severe periodontitis.
- Data was collected from dental examinations, including various predictive features (21 in total) such as demographics, oral health indicators, and medical histories, and analyzed using models like mixed-effects logistic regression and neural networks to improve screening efficiency.
Machine Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast Cancer.
JCO Clin Cancer Inform
March 2020
Mathematical Modeling for Oncology Team, Inria Bordeaux Sud-Ouest, Talence, France.
Purpose: For patients with early-stage breast cancer, predicting the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (eg, Cox regression). Here we define and evaluate the predictive ability of a mechanistic model for time to distant metastatic relapse.
View Article and Find Full Text PDFBupropion SR vs placebo for smoking cessation in health care professionals.
Am J Health Behav
July 2005
University Medical Policlinic, 10011 Lausanne, Switzerland.
Objective: To evaluate bupropion SR for smoking cessation in physicians and nurses.
Methods: This double-blind prospective 26-center, 12-country trial randomized 687 subjects to smoking cessation counselling with bupropion SR or placebo for 7 weeks. The participants were followed for 52 weeks.
Data on the study of the genetics of ischemic heart disease by means of the classical methods (genealogical, population-statistical, and twin) are discussed. The results of a follow-up, conducted for many years, of the condition of close relatives of sick and healthy probands, who were found to be practically healthy during the first examination are discussed. Clinical biochemical examination of the relatives of sick probands is considered a necessary stage in the further clinical study of the genetic factors in ischemic heart disease.
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