构建儿科社交沟通个性化多任务评估基准，保护隐私同时支持学习追踪与临床预测。
BuddyBench: A Privacy-Constrained Multi-Task Benchmark for Pediatric Social-Communication Personalization
- 整合学习轨迹、临床评估与自评数据，建立统一评估框架。
- 包含189名观察组和86名干预组儿童数据，支持知识追踪与因果推断。
- 提供合成数据集，保障隐私且可复现，适合医疗AI研究者使用。
BuddyBench 提出一个面向儿科社交沟通个性化的隐私约束型多任务评估基准。不同于以往以影像、基因或横断面临床表型为主的神经发育数据库，BuddyBench 在统一框架下关联了逐题学习轨迹、标准化临床评估、BuddyPlan自评量表及随机治疗结局。该基准包含两个队列：ND-03为观察队列，覆盖任务1-2，样本量n = 189；ND-02为随机对照试验队列，用于任务3-4，意向治疗分析样本量n = 86。整体支持知识追踪、下一题推荐、临床预测与因果推断，实现行为个性化与临床评价的联动。此外，引入BuddyBench-Sim合成伴侣数据集，确保评估可复现且保护儿童临床记录隐私。基线实验表明各任务均存在有效信号。
原文摘要 · Abstract (English)
BuddyBench introduces a privacy-constrained multi-task benchmark for pediatric social-communication personalization. Unlike existing neurodevelopmental repositories that primarily emphasize imaging, genetics, or cross-sectional clinical phenotyping, BuddyBench links drill-level learning trajectories, standardized clinical assessments, BuddyPlan self-report, and randomized-treatment endpoints within a unified benchmark schema. BuddyBench combines two cohorts: ND-03 is an observational cohort with dense drill coverage for Tasks1-2 (n = 189), and ND-02 is a randomized controlled trial cohort for Tasks3-4 (n = 86 ITT). Together, they support knowledge tracing, next-drill recommendation, clinical prediction, and causal inference, linking behavioral personalization to clinical evaluation. We additionally introduce BuddyBench-Sim, a synthetic companion dataset for reproducible evaluation. Baselines show signal across tasks while keeping pediatric clinical records protected.
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