Large language models (LLMs) can capture rich representations of concepts
that are useful for real-world tasks. However, language alone is limited. While
existing LLMs excel at text-based inferences, health applications require that
models be grounded in numerical data (e.g., vital signs, laboratory values in
clinical domains; steps, movement in the wellness domain) that is not easily or
readily expressed as text in existing training corpus. We demonstrate that with
only few-shot tuning, a large language model is capable of grounding various
physiological and behavioral time-series data and making meaningful inferences
on numerous health tasks for both clinical and wellness contexts. Using data
from wearable and medical sensor recordings, we evaluate these capabilities on
the tasks of cardiac signal analysis, physical activity recognition, metabolic
calculation (e.g., calories burned), and estimation of stress reports and
mental health screeners.

该研究展示大型语言模型能够通过只有少量的微调，在医疗和健康领域中处理不易表达为文本的各种生理和行为时间序列数据，并对心脏信号分析、身体活动识别、代谢计算和压力报告估计等健康任务进行有意义的推理。