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

项目摘要/摘要 在美国，医疗保健费用继续呈指数级增长，口腔疾病仍然是最大的费用之一 个人保健支出方面的10个类别。应对不断上升的医疗成本，并将 不必要的治疗，通过纳入患者的看法，越来越强调以患者为中心的护理 在医学和牙科研究中，与健康相关的生活质量评估是重要的健康结果。 口腔健康状况对个人的身体和心理都有影响，并影响他们的生活质量 他们是如何成长、长相、说话、咀嚼和社交的。解决口腔健康相关生活质量(OHRQOL)是 重要，将有助于提高护理质量，最大限度地减少口腔健康差距，提高患者满意度 和整体生活质量，并降低成本。尽管人力厅很重要，但很少有 青春期/青春期OHRQOL纵向和无轨迹研究理想情况下，这样的研究将 确定纵向因素和模式/轨迹，以更全面地了解人事厅的发展情况 个体进入成年期。要充分应对纵向数据的挑战并创建可预测的 模型捕获很多重要的轨迹行列式，需要用到一个高性能的算法 就像机器学习一样，这是一种人工智能。我们的研究将是发展机器学习的第一个 使用纵向数据预测人事厅生活质量的工具。我们选择机器学习是因为它可以 适应高维数据以准确预测个人的OHRQOL轨迹。我们会 利用我们爱荷华州氟化物研究的独特纵向数据，从受试者从出生到 现年23岁。人力厅的轨迹将使用三个因变量来定义，分别在17岁、19岁、 23：1)儿童知觉问卷，2)全球口腔健康，3)视觉模拟生活质量评分。到期 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)和有监督的机器学习(套索回归、随机森林和极限 梯度助推模型)分别用于轨迹分析和结果预测。具体目标 这项研究的目的是：1)确定从青春期晚期到成年初期的OHRQOL轨迹 无监督机器学习，以及2)使用监督识别轨迹组成员资格的预测器 机器学习。这项研究将大大有助于我们对青少年/年轻人的了解 人力厅的轨迹和决定因素。结果将为临床医生和政策制定者奠定基础 过渡到更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 健康差异，降低成本，提高患者满意度。我们的研究将介绍和展示 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Web的应用程序 临床医生和政策制定者可以用来更好地设计适合口腔健康的干预和治疗 个人和人口的需求。

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.