WEQA: 可穿戴健康问答中的查询自适应智能推理
WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning
AI总结：
提出WEQA框架，通过LLM控制器动态组合传感器分析与预训练模型，实现可穿戴健康数据问答，在基准测试中准确率提升24%，专家评估显示实用性和临床合理性显著提高。
摘要：
语言模型在医学问答中表现出色，有时甚至超过普通医生的准确率。然而，关于可穿戴健康数据的问题回答仍然具有挑战性且研究不足，因为这些无处不在的传感器产生连续、高维和纵向的数据，难以与LLM预训练中的文本中心分布对齐。传感器模态和用户意图的多样性无法通过固定的推理工作流或单一的预训练基础模型有效处理。为了解决这些挑战，我们提出了WEQA，一个查询自适应智能体框架，将LLM推理与专门的可穿戴分析和建模工具统一起来。采用LLM控制器来合成执行计划，动态地将每个查询路由到适当的传感器分析和预训练模型组合，并利用外部知识进行基于证据的响应审计。我们还整理了一个基准测试，涵盖四个开放的可穿戴数据集，包括三个不同健康领域的分析和预测任务。实验表明，我们的框架比LLM和智能体基线准确率提高24%，一项由12名医学专家和8名用户进行的盲法研究显示，在实用性和临床合理性方面有显著提升。
英文摘要：
Language models are remarkably capable at medical question answering, in some cases surpassing the accuracy of general physicians. However, answering questions about wearable health data remains challenging and understudied, as these ubiquitous sensors produce continuous, high-dimensional, and longitudinal data, which is non-trivial to align with text-centric distributions in LLM pretraining. The diversity of sensor modalities and user intents cannot be effectively handled by a fixed reasoning workflow or a single pretrained foundation model. To address these challenges, we propose WEQA, a query-adaptive agent framework that unifies LLM reasoning with specialized wearable analytical and modeling tools. An LLM controller is employed to synthesize execution plans and dynamically route each query to the appropriate combination of sensor analysis and pretrained models, and perform grounded response auditing with external knowledge. We also curate a benchmark spanning four open wearable datasets comprising analytic and predictive tasks in three different health domains. Experiments show that our framework is 24% more accurate than LLM and agentic baselines, and a blinded study with 12 medical experts and 8 users shows substantial gains in usefulness and clinical soundness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
