Development of a Simple Youth Diabetes Screening Tool Using Machine Learning

项目摘要 美国2型糖尿病患者的数量预计将以惊人的49 到2050年的百分比。然而，简单的筛查工具可以可靠地识别糖尿病风险并预防不良反应。 这种严重疾病的影响只对成年人有效，对青年人无效。事实上，我们的初步研究使用 来自国家健康和营养调查（NHANES）的全国代表性数据发现， 已发表的儿科临床指南在捕获患有糖尿病或其 前驱状态（前驱糖尿病）。为了应对这一紧迫的健康挑战，这项R21研究提案旨在 将临床、流行病学和数据科学专家聚集在一起，开发和验证青年糖尿病风险 筛选器。我们将开发一个方便使用的筛查工具，以识别患有糖尿病前期或糖尿病的青少年， 利用最先进的机器学习技术和来自NHANES的丰富数据。我们的最终产品将是一个 基于网络的筛选器，可集成到数字健康平台和传统公共卫生中 监督工作。这个翻译产品将帮助家长，社区组织，学校， 初级保健提供者准确识别有糖尿病风险的青少年， 明确的诊断测试，以及预防和医疗管理计划。 具体目标：1。我们将开发一个初步的候选筛选器，以区分正常和 糖尿病前期/糖尿病青年通过应用简约预测建模面向机器学习技术 NHANES数据。2.我们将开发更多的候选人筛选器，整合的各个领域， NHANES数据，并将通过比较所有人的表现， 考生3.为了说明社会人口因素对糖尿病前期/糖尿病筛查的重要性， 我们还将根据年龄、性别和性别， 种族/民族。我们将验证所有这些候选人，以开发一个准确的集成筛选器， 基于个人的社会人口统计特征（年龄、性别和种族/民族）对个人进行个性化。 最后，我们将实现一个用户友好的基于Web的版本的集成筛选器比可以帮助识别 青年糖尿病风险，并成为未来以社区为基础的青年糖尿病预防战略的一部分 在高风险社区实施。

Project Summary The number of youth with type 2 diabetes in the U.S. is projected to increase by a staggering 49 percent by 2050. However, simple screening tools to reliably identify diabetes risk and prevent the adverse effects of this serious disease are only available for adults, not for youth. Indeed, our preliminary studies using nationally representative data from the National Health and Nutrition Examination Survey (NHANES) found that published pediatric clinical guidelines performed relatively poorly in capturing youth with diabetes or its precursor condition (prediabetes). In response to this urgent health challenge, this R21 research proposal aims to bring together clinical, epidemiology and data science experts to develop and validate a youth diabetes risk screener. We will develop a user-friendly screening tool to identify youth with prediabetes or diabetes by leveraging state of the art machine learning techniques and rich data from NHANES. Our final product will be a web-based screener that can be integrated into both digital health platforms and traditional public health surveillance efforts. This translational product will aid parents, community-based organizations, schools, and primary care providers in accurately identifying youth at risk of diabetes who can benefit from subsequent definitive diagnostic testing, as well as prevention and medical management programs. Specific Aims: 1. We will develop an initial candidate screener to distinguish between normal and prediabetic/diabetic youth by applying parsimonious predictive modeling-oriented machine learning techniques to NHANES data. 2. We will develop additional candidate screeners that integrate the various domains of NHANES data, and will identify the best-performing screener for youth by comparing the performances of all the candidates. 3. To address the importance of sociodemographic factors for prediabetes/diabetes screening, we will also develop candidate screeners specific to sociodemographic subgroups based on age, sex and race/ethnicity. We will validate all these candidates to develop an integrated screener that is accurate and personalized to individuals based on their sociodemographic characteristics (age, sex and race/ethnicity). Finally, we will implement a user-friendly web-based version of the integrated screener than can help identify youth at risk of diabetes, and become part of a community-based youth diabetes prevention strategy for future implementation in high-risk communities.