基于上下文的医院化疗预测评估：利用LLMs进行决策支持
Context-Aware Hospitalization Forecasting Evaluations for Decision Support using LLMs
AI总结：
本文评估了利用LLMs进行医院化疗预测的方法，通过三种方法在60个县的数据上验证了上下文增强的混合模型在非平稳医疗资源预测中的有效性。
摘要：
医疗和公共卫生专家必须根据大型医疗中断（如运营故障或流行病）期间的预测住院趋势，做出实时资源决策，如扩大医院床位容量。预测模型可以通过分析设施层面的大规模资源相关数据来协助此任务，但必须在真实世界数据条件下可靠。最近的研究表明，大型语言模型（LLMs）可以将更丰富的上下文纳入数值预测。尽管传统模型主要依赖时间上下文（即过去的观测），LLMs还可以利用非时间性的公共卫生上下文，如人口统计学、地理和人口层面的特征。然而，仍不清楚这些模型应如何在真实世界医疗环境中使用以产生稳定或决策相关的预测。为了评估LLMs在此设置中的有效使用，我们评估了三种方法：直接基于LLMs的预测、传统时间序列模型以及一种结合LLM衍生信号的结构化模型（HybridARX）。由于目标是操作决策而非仅最小化误差，我们除了标准预测指标外，还评估了偏倚和领先滞后对齐。我们的结果表明，HybridARX在传统ARX上有所改进，尤其是在将嘈杂的上下文信号纳入结构化时间序列模型时，产生了更稳定和更好的校准预测。这些发现表明，在非平稳医疗资源预测中，LLMs在嵌入结构化混合模型时最有用。
英文摘要：
Medical and public health experts must make real-time resource decisions, such as expanding hospital bed capacity, based on projected hospitalization trends during large-scale healthcare disruptions (e.g., operational failures or pandemics). Forecasting models can assist in this task by analyzing large volumes of resource-related data at the facility level, but they must be reliable for decision-making under real-world data conditions. Recent work shows that large language models (LLMs) can incorporate richer forms of context into numerical forecasting. Whereas traditional models rely primarily on temporal context (i.e., past observations), LLMs can also leverage non-temporal public health context such as demographic, geographic, and population-level features. However, it remains unclear how these models should be used to produce stable or decision-relevant predictions in real-world healthcare settings. To evaluate how LLMs can be effectively used in this setting, we evaluate three approaches across 60 counties with low-,mid-, and high-hospitalization intensities in the United States: direct LLM-based forecasting, classical time-series models, and a context-augmented hybrid pipeline (HybridARX) that incorporates LLM-derived signals into structured models. Because the goal is operational decision-making rather than error minimization alone, we evaluate performance with bias and lead-lag alignment in addition to standard forecasting metrics. Our results show that HybridARX improves over classical ARX by yielding more stable and better-calibrated forecasts, particularly when incorporating noisy contextual signals into structured time-series models. These findings suggest that, in non-stationary healthcare resource forecasting, LLMs are most useful when embedded within structured hybrid models.
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