📄 中文摘要
可穿戴设备每天生成1万个数据点，而医生每季度仅查看一个数字，这种差距导致数百万美元的可预防健康危机。时间序列大语言模型（LLMs）是能够从身体数据中学习的人工智能，关注的是数据随时间的变化，而非单一快照。将身体比作一部Netflix系列剧，穿戴设备和实验室测试则是逐渐展开的剧集和场景。时间序列LLMs能够理解整个故事，识别出单一帧可能遗漏的模式、角色发展和情节转折。通过逐步讲解，读者将理解这些系统的工作原理及其构建方法。
时间序列大语言模型：50000美元的健康智能差距
出处: Time-Series LLMs: The $50k Health Intelligence Gap
发布: 2026年3月15日
📄 English Summary
Time-Series LLMs: The $50k Health Intelligence Gap
Wearable devices generate 10,000 data points daily, while doctors only see one number per quarter, resulting in millions of dollars lost to preventable health crises. Time-series LLMs are AIs that learn from bodily data over time rather than relying on single snapshots. The body is likened to a Netflix series, where wearables and lab tests serve as episodes and scenes unfolding over days, weeks, and months. Time-series LLMs are designed to comprehend the entire narrative, identifying patterns, character arcs, and plot twists that might be missed when only analyzing a single frame. A step-by-step explanation will help readers understand how these systems operate and how to begin building them.