确定儿童肥胖的关键决定因素：机器学习研究的叙述性回顾,Childhood Obesity

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确定儿童肥胖的关键决定因素：机器学习研究的叙述性回顾
Childhood Obesity ( IF 1.5 ) Pub Date : 2021-04-07 , DOI: 10.1089/chi.2020.0324
Madison N LeCroy ₁ , Ryung S Kim ₁ , June Stevens _{2,

3} , David B Hanna ₁ , Carmen R Isasi ₁

Affiliation

机器学习是一类能够处理具有潜在非线性关系的大量预测变量的算法。通过将机器学习应用于肥胖，研究人员可以检查多种环境中的风险因素（例如，学校和家庭）相互作用以最好地预测儿童肥胖风险。在这篇叙述性评论中，我们概述了应用机器学习结合社会人口学和行为风险因素来预测儿童肥胖的研究。目的是总结现有机器学习研究中确定的肥胖的关键决定因素，并强调该领域未来机器学习应用的机会。在 15 项同行评审研究中，大约一半检查了儿童早期（0-24 个月大）的决定因素。这些研究确定了儿童的体重史（例如，超重/肥胖史或出生至 24 个月大期间体重相关指标大幅增加）和父母超重/肥胖（当前或之前）是关键风险因素，而其余研究表明，社会因素和缺乏身体活动在儿童中期和儿童后期/青春期。跨年龄组的研究结果表明，可能需要特定种族/民族的模型来准确预测从儿童中期开始的肥胖。未来的研究应考虑使用现有的大型数据集来利用机器学习的优势，并应收集更广泛的新风险因素（例如，健康的心理社会和社会文化决定因素）以更好地预测儿童肥胖。最终，此类研究可以帮助开发有效的肥胖预防干预措施，特别是那些解决种族/少数民族所经历的不成比例的肥胖负担的干预措施。

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Identifying Key Determinants of Childhood Obesity: A Narrative Review of Machine Learning Studies

Machine learning is a class of algorithms able to handle a large number of predictors with potentially nonlinear relationships. By applying machine learning to obesity, researchers can examine how risk factors across multiple settings (e.g., school and home) interact to best predict childhood obesity risk. In this narrative review, we provide an overview of studies that have applied machine learning to predict childhood obesity using a combination of sociodemographic and behavioral risk factors. The objective is to summarize the key determinants of obesity identified in existing machine learning studies and highlight opportunities for future machine learning applications in the field. Of 15 peer-reviewed studies, approximately half examined early childhood (0–24 months of age) determinants. These studies identified child's weight history (e.g., history of overweight/obesity or large increases in weight-related measures between birth and 24 months of age) and parental overweight/obesity (current or prior) as key risk factors, whereas the remaining studies indicated that social factors and physical inactivity were important in middle childhood and late childhood/adolescence. Across age groups, findings suggested that race/ethnic-specific models may be needed to accurately predict obesity from middle childhood onward. Future studies should consider using existing large data sets to take advantage of the benefits of machine learning and should collect a wider range of novel risk factors (e.g., psychosocial and sociocultural determinants of health) to better predict childhood obesity. Ultimately, such research can aid in the development of effective obesity prevention interventions, particularly ones that address the disproportionate burden of obesity experienced by racial/ethnic minorities.

更新日期：2021-04-09

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