This document proposes an algorithm for a mobile application designed to monitor multidimensional child growth through digital phenotyping. Digital phenotyping offers a unique opportunity to collect and analyze high-frequency data in real time, capturing behavioral, psychological, and physiological states of children in naturalistic settings. Traditional models of child growth primarily focus on physical metrics, often overlooking multidimensional aspects such as emotional, social, and cognitive development. In this paper, we introduce a Bayesian artificial intelligence (AI) algorithm that leverages digital phenotyping to create a Multidimensional Index of Child Growth (MICG). This index integrates data from various dimensions of child development, including physical, emotional, cognitive, and environmental factors. By incorporating probabilistic modeling, the proposed algorithm dynamically updates its learning based on data collected by the mobile app used by mothers and children. The app also infers uncertainty from response times, adjusting the importance of each dimension of child growth accordingly. Our contribution applies state-of-the-art technology to track multidimensional child development, enabling families and healthcare providers to make more informed decisions in real time.

翻译：本文提出一种用于移动应用程序的算法，旨在通过数字表型分析监测儿童的多维成长。数字表型分析为实时收集和分析高频数据提供了独特机会，能够捕捉自然情境下儿童的行为、心理和生理状态。传统的儿童成长模型主要关注身体指标，往往忽视情感、社交和认知发展等多维层面。本文介绍一种贝叶斯人工智能算法，该算法利用数字表型分析构建儿童成长多维指数。该指数整合了儿童发展多个维度的数据，包括身体、情感、认知及环境因素。通过引入概率建模，所提算法能基于母亲与儿童使用的移动应用所收集的数据动态更新学习过程。该应用还能根据响应时间推断不确定性，从而相应调整儿童成长各维度的重要性权重。我们的研究应用前沿技术追踪儿童多维发展，使家庭和医疗保健提供者能够实时做出更明智的决策。