发表机构
Future Health Technologies, Singapore-ETH Centre; Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital; Geriatric Medicine, Khoo Teck Puat Hospital; Department of Orthopedic Surgery, Woodlands Health Campus（未来健康技术中心，新加坡-ETH中心; 陈笃生医院老年病学与 active 老龄化研究所; 邱德拔医院老年医学科; 伍德兰兹健康园区骨科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究采用TabPFN的上下文学习方法，在小规模数据集上较传统模型更优，通过SHAP-IQ揭示高阶特征交互对行动受限老年人感知社区步行便利性的影响。
AI 中文摘要
随着全球人口老龄化，通过包容性城市设计提升社区步行便利性，对缓解建成环境（BE）障碍、减少阻碍老年人身体活动与社会参与的因素具有重要意义。本研究探究上下文学习（ICL）的效用，采用基于Transformer的基础模型TabPFN，确定建成环境特征如何影响感知步行便利性，该感知通过社区环境步行量表（NEWS-A）调查测量。研究使用包含257名独特人口统计特征（患有膝骨关节炎或有跌倒史的老年人）的小规模数据集，TabPFN在采用等宽分箱法将步行便利性感知分为低、中、高三类时，达到54.89%的宏F1分数，该结果优于经网格搜索优化的基线模型，包括随机森林（45.85%）和XGBoost（50.56%）。为解释这些结果，本研究采用Shapley交互量化（SHAP-IQ）识别特征交互的层级重要性，初步结果显示模型的预测逻辑主要由高阶交互驱动，例如，平均街道迂回度与可驾驶道路比例的交互成为感知步行便利性的主要区分因素；社区绿化仅在与个人对跌倒的恐惧或对年龄友好性的感知结合时，才具有显著预测重要性。总体而言，使用TabPFN的ICL在小规模数据集上表现出优越性能，提升了所得解释性见解的保真度，此外，SHAP-IQ为高阶特征交互如何驱动模型预测提供了协同视角。
英文摘要
As global populations age, enhancing neighborhood walkability through inclusive urban design is important for mitigating built environment (BE) barriers that discourage physical activity and social participation among older adults. This study investigates the utility of in-context learning (ICL), using the transformer-based foundation model TabPFN, to determine how BE features influence perceived walkability, as measured by the Neighborhood Environment Walkability Scale (NEWS-A) survey. Using a small-scale dataset (N = 257) comprising a unique demographic of older adults with knee osteoarthritis or a history of falls, TabPFN achieved a macro F1 score of 54.89% for walkability perceptions categorized as Low, Neutral, and High using equal-width binning. This result outperformed optimized, grid-searched baseline models, including Random Forest (45.85%) and XGBoost (50.56%). To interpret these results, we employed Shapley Interaction Quantification (SHAP-IQ) to identify the hierarchical importance of feature interactions. Preliminary results revealed that the model's predictive logic was primarily driven by higher-order interactions. For example, the interaction between average street circuity and the ratio of drivable roads emerged as the primary discriminator of perceived walkability. Neighborhood greenery was found to have substantial predictive importance only when combined with an individual's fear of falling or perception of age-friendliness. Overall, ICL using TabPFN demonstrates superior performance on small-scale datasets, enhancing the fidelity of the resulting interpretive insights. Furthermore, SHAP-IQ provides a synergistic perspective on how higher-order feature interactions drive the model's predictions.
Comments8 pages, 2 tables, 1 figure
Journal refCOSIT 2026 Poster Paper
