用深度自编码器实现可预测的生长曲线建模，兼顾效率与可解释性。
Deep-SITAR: A SITAR-Based Deep Learning Framework for Growth Curve Modeling via Autoencoders
- 融合自编码器与B样条，通过编码器估计个体随机效应
- 新个体生长轨迹预测无需重估模型，效率显著提升
- 适合需要快速个性化生长预测的研究场景
为捕捉人类生长的复杂非线性特征，已有多种方法被提出。其中广泛应用的是基于形变不变的混合效应模型SITAR，该模型使用自然立方样条表示群体共享生长模式，并引入三个个体特异性随机效应——时序、大小和生长强度，以刻画个体差异。本文提出一种基于自编码器架构的监督式深度学习框架Deep-SITAR，将深度神经网络与B样条模型结合，实现对SITAR模型的估计。其中，编码器用于估计每个个体的随机效应，解码器则基于B样条完成类似经典SITAR的拟合。该方法使新个体进入群体后可直接预测其随机效应，无需重新估计整个模型。Deep-SITAR有效结合了深度学习的灵活性与传统混合效应模型的可解释性，为生长轨迹预测提供高效且可解释的新路径。
原文摘要 · Abstract (English)
Several approaches have been developed to capture the complexity and nonlinearity of human growth. One widely used is the Super Imposition by Translation and Rotation (SITAR) model, which has become popular in studies of adolescent growth. SITAR is a shape-invariant mixed-effects model that represents the shared growth pattern of a population using a natural cubic spline mean curve while incorporating three subject-specific random effects -- timing, size, and growth intensity -- to account for variations among individuals. In this work, we introduce a supervised deep learning framework based on an autoencoder architecture that integrates a deep neural network (neural network) with a B-spline model to estimate the SITAR model. In this approach, the encoder estimates the random effects for each individual, while the decoder performs a fitting based on B-splines similar to the classic SITAR model. We refer to this method as the Deep-SITAR model. This innovative approach enables the prediction of the random effects of new individuals entering a population without requiring a full model re-estimation. As a result, Deep-SITAR offers a powerful approach to predicting growth trajectories, combining the flexibility and efficiency of deep learning with the interpretability of traditional mixed-effects models.
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