Predicting childhood obesity using electronic health records and publicly available data

BackgroundBecause of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective strategies are needed to address this condition in early childhood. The ability to predict obesity before age five could be a useful tool, allowing prevention strategies to focus on high risk children. The few existing prediction models for obesity in childhood have primarily employed data from longitudinal cohort studies, relying on difficult to collect data that are not readily available to all practitioners. Instead, we utilized real-world unaugmented electronic health record (EHR) data from the first two years of life to predict obesity status at age five, an approach not yet taken in pediatric obesity research.Methods and findingsWe trained a variety of machine learning algorithms to perform both binary classification and regression. Following previous studies demonstrating different obesity determinants for boys and girls, we similarly developed separate models for both groups. In each of the separate models for boys and girls we found that weight for length z-score, BMI between 19 and 24 months, and the last BMI measure recorded before age two were the most important features for prediction. The best performing models were able to predict obesity with an Area Under the Receiver Operator Characteristic Curve (AUC) of 81.7% for girls and 76.1% for boys.ConclusionsWe were able to predict obesity at age five using EHR data with an AUC comparable to cohort-based studies, reducing the need for investment in additional data collection. Our results suggest that machine learning approaches for predicting future childhood obesity using EHR data could improve the ability of clinicians and researchers to drive future policy, intervention design, and the decision-making process in a clinical setting.研究背景：鉴于儿童肥胖与成人肥胖共病之间存在强关联，且成年后降低体质量指数（Body Mass Index，BMI）的难度较大，因此亟需制定有效策略以应对幼儿期的肥胖问题。在五岁前预测肥胖风险可成为一项实用工具，使预防策略能够聚焦于高风险儿童。现有为数不多的儿童肥胖预测模型主要采用纵向队列研究数据，依赖于难以收集且并非所有从业者都能轻易获取的资料。与之不同，我们利用儿童生命最初两年的真实世界非增强电子健康档案（Electronic Health Record，EHR）数据来预测五岁时的肥胖状态，这一方法在儿童肥胖研究中尚未被采用。研究方法与结果：我们训练了多种机器学习算法以完成二分类与回归任务。既往研究表明男孩与女孩的肥胖影响因素存在差异，我们同样为两个群体分别构建了预测模型。在针对男孩和女孩的独立模型中，我们发现身长别体重z评分、19至24个月龄间的体质量指数，以及两岁前记录的最后一次体质量指数测量值是预测中最为重要的特征。表现最优的模型能够实现的受试者工作特征曲线下面积（Area Under the Receiver Operator Characteristic Curve，AUC）分别为女孩81.7%、男孩76.1%。研究结论：我们借助电子健康档案数据实现了对五岁儿童肥胖状态的预测，其受试者工作特征曲线下面积与基于队列研究的相关研究相当，同时减少了为额外数据收集所投入的资源。我们的研究结果表明，利用电子健康档案数据预测儿童未来肥胖风险的机器学习方法，能够提升临床医生与研究人员制定后续政策、设计干预措施以及开展临床场景决策的能力。