Developmental surveillance tools are used to closely monitor the early development of infants and young children. This study provides a novel implementation of a multidimensional item response model, using Bayesian hierarchical priors, to construct developmental profiles for a small sample of children (N = 115) with sparse data collected through an online developmental surveillance tool. The surveillance tool records 348 developmental milestones measured from birth to three years of age, within six functional domains: auditory, hands, movement, speech, tactile, and vision. The profiles were constructed in three steps: (1) the multidimensional item response model, embedded in the Bayesian hierarchical framework, was implemented in order to measure both the latent abilities of the children and attributes of the milestones, while retaining the correlation structure among the latent developmental domains; (2) subsequent hierarchical clustering of the multidimensional ability estimates enabled identification of subgroups of children; and (3) information from the posterior distributions of the item response model parameters and the results of the clustering were used to construct a personalized profile of development for each child. These individual profiles support early identification of, and personalized early interventions for, children with developmental delay.

发育监测工具用于密切监测婴幼儿的早期发育。本研究提供了一种多维项目响应模型的新颖实现，使用贝叶斯分层先验，通过在线发育监测工具收集的稀疏数据，为一小部分儿童（N = 115）构建发育概况。该监测工具记录了从出生到三岁的 348 个发育里程碑，涵盖六个功能领域：听觉、手、运动、言语、触觉和视觉。档案的构建分三个步骤：（1）嵌入贝叶斯分层框架的多维项目反应模型，以衡量儿童的潜在能力和里程碑的属性，同时保留项目之间的相关结构。潜在的发展领域；(2) 随后对多维能力估计进行层次聚类，从而能够识别儿童亚组；(3)使用项目反应模型参数的后验分布信息和聚类结果来构建每个孩子的个性化发展概况。这些个人资料支持对发育迟缓儿童的早期识别和个性化早期干预。