Trajectories/Predictors of Oral Health-Related Quality of Life to Early Adulthood

成年早期口腔健康相关生活质量的轨迹/预测因素

基本信息

批准号：
10524262
负责人：
STEVEN M. LEVY
金额：
$ 16.95万
依托单位：
UNIVERSITY OF IOWA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10524262
关键词：
Address Adolescence Adolescent Adolescent and Young Adult Adult Age Algorithms Artificial Intelligence Birth Caring Categories Child Client satisfaction Cross-Sectional Studies Data Data Analyses Data Scientist Dental Dental Research Dental caries Dentistry Development Dimensions Fluorides Future Goals Grant Health Health Care Costs Health Expenditures High Prevalence Individual Iowa Longitudinal Studies Machine Learning Malocclusion Measures Medical Care Costs Medical Research Methods Modeling Mouth Diseases Odds Ratio Online Systems Oral health Outcome Participant Patient-Centered Care Patients Pattern Perception Population Positioning Attribute Public Health Quality of Care Quality of life Quality-Adjusted Life Years Quality-of-Life Assessment Questionnaires Research Risk Factors Socialization Specialist Statistical Data Interpretation Statistical Models Study Subject Time United States Visual analog care costs cost data complexity depressive symptoms emerging adult experience fluorosis gradient boosting health disparity health related quality of life improved innovation machine learning algorithm machine learning model multidimensional data outcome prediction patient oriented predictive modeling programs psychologic random forest supervised learning therapy design tool unnecessary treatment unsupervised learning young adult

项目摘要

PROJECT SUMMARY/ABSTRACT Healthcare costs continue to grow exponentially in the United States and oral diseases remain one of the top 10 categories in terms of personal health care expenditures. To tackle the rising costs of care and minimize unnecessary treatment, there is increasing emphasis on patient-centered care, by including patient perceptions and health-related quality of life assessments as important health outcomes in medical and dental research. Oral health conditions have physical and psychological effects on individuals and influence their quality of life - how they grow, look, speak, chew, and socialize. Addressing Oral Health-Related Quality of Life (OHRQoL) is important and will help improve the quality of care, minimize oral health disparities, improve patient satisfaction and overall quality of life, and reduce costs. Despite the importance of OHRQoL, there have been few longitudinal and no trajectory studies of OHRQoL in adolescence/young adulthood. Ideally, such studies would identify longitudinal factors and patterns/trajectories to more fully understand development of OHRQoL as individuals enter adulthood. To adequately address the challenges of longitudinal data and create a predictive model capturing the many important trajectory determinants, it is necessary to use a high-performing algorithm like machine learning, a type of artificial intelligence. Our study will be the first to develop machine learning tools for prediction of OHRQoL using longitudinal data. We chose machine learning because it can accommodate the high-dimensional data to accurately predict individuals’ OHRQoL trajectories. We will leverage unique longitudinal data from our Iowa Fluoride Study, with data from subjects followed from birth to age 23 years. OHRQoL trajectories will be defined using three dependent variables measured at ages 17, 19, and 23: 1) Child Perception Questionnaire, 2) global oral health, and 3) visual analog quality of life scores. Due to the complexity and high dimensionality of the data, we will use unsupervised machine learning (K-means for longitudinal data) and supervised machine learning (LASSO regression, random forest and extreme gradient-boosting model) for the trajectory analysis and outcome predictions, respectively. The specific aims of the study will be to 1) determine the OHRQoL trajectories from late adolescence to young adulthood using unsupervised machine learning, and 2) identify predictors of trajectory group membership using supervised machine learning. The study will contribute significantly to our knowledge of adolescents’/young adults’ OHRQoL trajectories and determinants. The outcomes will set the stage for clinicians and policymakers to transition to a care model that is more patient-centered, which will improve oral health outcomes, reduce oral health disparities, reduce costs, and increase patient satisfaction. Our research will introduce and showcase the usefulness of machine learning in oral health research. Long term, we will develop a web-based application that clinicians and policymakers can use to better design interventions and treatments to suit the oral health needs of individuals and populations.

项目概要/摘要美国的医疗保健费用继续呈指数级增长，口腔疾病仍然是最严重的疾病之一个人医疗保健支出分为10类，以应对不断上涨的医疗费用并最大限度地减少。不必要的治疗，越来越强调以患者为中心的护理，包括患者的看法与健康相关的生活质量评估作为医学和牙科研究中的重要健康结果。口腔健康状况对个人的身体和心理有影响，并影响他们的生活质量 - 他们的生长、外观、说话、咀嚼和社交方式是解决口腔健康相关生活质量 (OHRQoL) 的问题。重要，将有助于提高护理质量，最大限度地减少口腔健康差异，提高患者满意度尽管 OHRQoL 很重要，但是却很少有。理想情况下，此类研究应该对青春期/成年早期的 OHRQoL 进行纵向研究和无轨迹研究。确定纵向因素和模式/轨迹，以更全面地了解 OHRQoL 的发展个人进入成年期，以充分应对纵向数据的挑战并创建预测。模型捕获了许多重要的轨迹决定因素，因此有必要使用高性能算法就像机器学习一样，我们的研究将首先开发机器学习。使用纵向数据预测 OHRQoL 的工具我们选择机器学习，因为它可以。我们将容纳高维数据来准确预测个体的 OHRQoL 轨迹。利用我们爱荷华州氟化物研究的独特纵向数据，以及受试者从出生到 23 岁时的 OHRQoL 轨迹将使用 17 岁、19 岁、 23：1) 儿童认知问卷，2) 全球口腔健康，3) 视觉模拟生活质量评分。针对数据的复杂性和高维性，我们将使用无监督机器学习（K-means for 纵向数据）和监督机器学习（LASSO 回归、随机森林和极端梯度提升模型）分别用于轨迹分析和结果预测。该研究的目的是 1) 使用以下方法确定从青春期晚期到成年早期的 OHRQoL 轨迹：无监督机器学习，2）使用有监督识别轨迹组成员资格的预测因子机器学习将极大地促进我们对青少年/年轻人的了解。 OHRQoL 轨迹和决定因素将为精英和政策制定者奠定基础。过渡到更加以患者为中心的护理模式，这将改善口腔健康结果，减少口腔我们的研究将介绍和展示健康差异、降低成本并提高患者满意度。机器学习在口腔健康研究中的作用从长远来看，我们将开发一个基于网络的应用程序。大象和政策制定者可以利用它来更好地设计适合口腔健康的干预措施和治疗方法个人和人群的需求。