Despite the proliferation of wearable health trackers and the importance of
sleep and exercise to health, deriving actionable personalized insights from
wearable data remains a challenge because doing so requires non-trivial
open-ended analysis of these data. The recent rise of large language model
(LLM) agents, which can use tools to reason about and interact with the world,
presents a promising opportunity to enable such personalized analysis at scale.
Yet, the application of LLM agents in analyzing personal health is still
largely untapped. In this paper, we introduce the Personal Health Insights
Agent (PHIA), an agent system that leverages state-of-the-art code generation
and information retrieval tools to analyze and interpret behavioral health data
from wearables. We curate two benchmark question-answering datasets of over
4000 health insights questions. Based on 650 hours of human and expert
evaluation we find that PHIA can accurately address over 84% of factual
numerical questions and more than 83% of crowd-sourced open-ended questions.
This work has implications for advancing behavioral health across the
population, potentially enabling individuals to interpret their own wearable
data, and paving the way for a new era of accessible, personalized wellness
regimens that are informed by data-driven insights.

我们介绍了一种名为个人健康洞察代理（Personal Health Insights Agent，PHIA）的代理系统，它利用最新的代码生成和信息检索工具来分析和解释可穿戴设备中的行为健康数据。该研究对于推进行为健康在人群中的发展具有影响，有可能使个人能够解释自己的可穿戴设备数据，并为基于数据驱动的洞察力所支持的新型可获得的、个性化的健康管理方法铺平道路。

利用大型语言模型代理将可穿戴设备数据转化为健康信息

Transforming Wearable Data into Health Insights using Large Language  Model Agents

Passively collected behavioral health data from ubiquitous sensors holds
significant promise to provide mental health professionals insights from
patient's daily lives; however, developing analysis tools to use this data in
clinical practice requires addressing challenges of generalization across
devices and weak or ambiguous correlations between the measured signals and an
individual's mental health. To address these challenges, we take a novel
approach that leverages large language models (LLMs) to synthesize clinically
useful insights from multi-sensor data. We develop chain of thought prompting
methods that use LLMs to generate reasoning about how trends in data such as
step count and sleep relate to conditions like depression and anxiety. We first
demonstrate binary depression classification with LLMs achieving accuracies of
61.1% which exceed the state of the art. While it is not robust for clinical
use, this leads us to our key finding: even more impactful and valued than
classification is a new human-AI collaboration approach in which clinician
experts interactively query these tools and combine their domain expertise and
context about the patient with AI generated reasoning to support clinical
decision-making. We find models like GPT-4 correctly reference numerical data
75% of the time, and clinician participants express strong interest in using
this approach to interpret self-tracking data.

使用大型语言模型（LLMs）综合多传感器数据生成临床有用的洞察力，首次展示使用 LLMs 进行二元抑郁症分类的准确性超过了现有技术，而且还强调了人工智能与医生领域专业知识和患者背景相结合的人工智能协同方法在临床决策支持方面的价值。