The correlation between children's personal and family characteristics (e.g., demographics and socioeconomic status) and their physical and mental health status has been extensively studied across various research domains, such as public health, medicine, and data science. Such studies can provide insights into the underlying factors affecting children's health and aid in the development of targeted interventions to improve their health outcomes. However, with the availability of multiple data sources, including context data (i.e., the background information of children) and motion data (i.e., sensor data measuring activities of children), new challenges have arisen due to the large-scale, heterogeneous, and multimodal nature of the data. Existing statistical hypothesis-based and learning model-based approaches have been inadequate for comprehensively analyzing the complex correlation between multimodal features and multi-dimensional health outcomes due to the limited information revealed. In this work, we first distill a set of design requirements from multiple levels through conducting a literature review and iteratively interviewing 11 experts from multiple domains (e.g., public health and medicine). Then, we propose HealthPrism, an interactive visual and analytics system for assisting researchers in exploring the importance and influence of various context and motion features on children's health status from multi-level perspectives. Within HealthPrism, a multimodal learning model with a gate mechanism is proposed for health profiling and cross-modality feature importance comparison. A set of visualization components is designed for experts to explore and understand multimodal data freely. We demonstrate the effectiveness and usability of HealthPrism through quantitative evaluation of the model performance, case studies, and expert interviews in associated domains.

翻译：摘要：儿童个人及家庭特征（如人口统计信息与社会经济地位）与其身心健康状况之间的关联性，已在公共卫生、医学和数据科学等研究领域得到广泛探讨。此类研究有助于揭示影响儿童健康的潜在因素，并为制定针对性干预措施以改善其健康结局提供依据。然而，随着多源数据的涌现（包括情境数据，即儿童背景信息；以及运动数据，即测量儿童活动的传感器数据），大规模、异质性和多模态数据的特性带来了新的挑战。现有基于统计假设与学习模型的方法因信息揭示有限，难以全面分析多模态特征与多维健康结局之间的复杂关联。本研究首先通过文献综述及对来自多个领域（如公共卫生和医学）的11位专家进行迭代访谈，从多个层面提炼出一组设计需求。继而提出HealthPrism——一个交互式可视分析系统，旨在帮助研究者从多层次视角探索各类情境与运动特征对儿童健康状况的重要性及其影响。系统中提出了具有门控机制的多模态学习模型用于健康特征分析及跨模态特征重要性比较，并设计了可视化组件套件，使专家能够自由探索和理解多模态数据。通过模型性能的定量评估、案例研究及跨领域专家访谈，我们验证了HealthPrism的有效性与可用性。