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

• 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
• 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

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.

项目摘要/摘要 在美国，医疗保健费用继续成倍增长，口腔疾病仍然是最重要的 关于个人卫生保健支出的10个类别。解决不断上升的护理成本并最小化 不必要的治疗，通过包括患者的看法，对以患者为中心的护理的重视 与健康相关的生活质量评估是医学和牙科研究中重要的健康成果。 口腔健康状况对个人有身体和心理影响并影响其生活质量 - 他们如何成长，外观，说话，咀嚼和社交。解决与口腔健康相关的生活质量（OHRQOL）是 重要的，并将有助于提高护理质量，最大程度地减少口腔健康差异，提高患者满意度 以及整体生活质量，并降低成本。尽管Ohrqol很重要，但很少 在青少年/年轻成年中，Ohrqol的纵向和没有轨迹研究。理想情况下，这样的研究将 确定纵向因素和模式/轨迹，以更充分地理解Ohrqol的发展 个人进入成年。充分解决纵向数据的挑战并创建预测 捕获许多重要轨迹确定的模型，必须使用高性能算法 像机器学习，一种人工智能。我们的研究将是第一个开发机器学习的人 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 容纳高维数据，以准确预测个体的OHRQOL轨迹。我们将 利用我们爱荷华州氟化物研究的独特纵向数据，并从出生到受试者的数据 年龄23岁。 OHRQOL轨迹将使用在17、19，19， 23：1）儿童感知问卷，2）全球口服健康和3）视觉模拟生活质量得分。到期的 对于数据的复杂性和高维度，我们将使用无监督的机器学习（K-均值 纵向数据）和监督机器学习（拉索回归，随机森林和极端 梯度增强模型）分别用于轨迹分析和结果预测。具体目标 该研究将是1）确定从青春期晚期到年轻成人的OHRQOL轨迹 无监督的机器学习，2）使用监督确定轨迹组成员资格的预测指标 机器学习。这项研究将为我们对青少年/年轻人的了解做出重大贡献。 OHRQOL轨迹和确定词。结果将为临床医生和政策制定者奠定舞台 过渡到以患者为中心的护理模型，这将改善口服健康结果，减少口服 健康差异，降低成本并提高患者满意度。我们的研究将介绍和展示 机器学习在口腔健康研究中的有用性。长期，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干预措施和治疗以适合口腔健康 个人和人口的需求。