使用多任务高斯过程对体重指数轨迹的前瞻性预测
Prospective Prediction of Body Mass Index Trajectories using Multi-task Gaussian Processes
AI总结：
本研究使用多任务高斯过程模型MagmaClust，基于历史生长数据前瞻性预测儿童BMI轨迹，在准确性、鲁棒性和超重风险预测上优于传统模型。
摘要：
临床医生经常研究儿童的体重指数（BMI）轨迹，以评估其相对于同龄人的生长情况，并预测未来的生长和疾病风险。虽然BMI轨迹的回顾性建模一直是一个活跃的研究领域，但从历史生长数据中对连续BMI轨迹进行前瞻性预测尚未得到充分研究。利用一个纵向母子队列中从出生到10岁的体重和身高测量数据，我们采用了一种名为MagmaClust的多任务高斯过程模型，来推导不同预测周期内BMI轨迹的概率性预测。实验评估了预测准确性、对缺失值的敏感性以及聚类数量。结果与三次B样条回归和参数化Jenss-Bayley混合效应模型进行了比较。还提出并评估了一个计算个体超重概率的下游工具。在所有实验中，MagmaClust在预测准确性上优于传统模型，并且无论缺失数据量（高达90%缺失）或预测周期（未来2至8年）如何，都能正确校准不确定性。此外，从MagmaClust的不确定性量化计算出的超重概率在预测10岁超重状态方面表现出高特异性（0.94至0.96）和高准确性（0.86至0.94），即使从2岁开始预测也是如此。MagmaClust提供了一个概率性非参数框架，用于前瞻性预测BMI轨迹，该框架对缺失值具有鲁棒性，并优于传统的BMI轨迹建模方法。它还对个体进行聚类，以识别儿童期的典型BMI模式（早期峰值、脂肪反弹）。总体而言，我们展示了其在预测整个儿童期BMI演变方面的潜力，使临床医生能够实施预防策略。
英文摘要：
Clinicians often investigate the body mass index (BMI) trajectories of children to assess their growth with respect to their peers, as well as to anticipate future growth and disease risk. While retrospective modelling of BMI trajectories has been an active area of research, prospective prediction of continuous BMI trajectories from historical growth data has not been well investigated. Using weight and height measurements from birth to age 10 years from a longitudinal mother-offspring cohort, we leveraged a multi-task Gaussian processes model, called MagmaClust, to derive probabilistic predictions for BMI trajectories over various forecasting periods. Experiments were conducted to evaluate the accuracy, sensitivity to missing values, and number of clusters. The results were compared with cubic B-spline regression and a parametric Jenss-Bayley mixed effects model. A downstream tool computing individual overweight probabilities was also proposed and evaluated. In all experiments, MagmaClust outperformed conventional models in prediction accuracy while correctly calibrating uncertainty regardless of the missing data amount (up to 90\% missing) or the forecasting period (from 2 to 8 years in the future). Moreover, the overweight probabilities computed from MagmaClust's uncertainty quantification exhibited high specificity ($0.94$ to $0.96$) and accuracy ($0.86$ to $0.94$) in predicting the 10-year overweight status even from age 2 years. MagmaClust provides a probabilistic non-parametric framework to prospectively predict BMI trajectories, which is robust to missing values and outperforms conventional BMI trajectory modelling approaches. It also clusters individuals to identify typical BMI patterns (early peak, adiposity rebounds) during childhood. Overall, we demonstrated its potential to anticipate BMI evolution throughout childhood, allowing clinicians to implement prevention strategies.
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