用轻量时序模型提升住院时长预测精度与效率
StayLTC: A Cost-Effective Multimodal Framework for Hospital Length of Stay Forecasting
- 基于连续时间递归的LTC网络，融合电子病历与临床文本
- 在MIMIC-III数据集上超越多数时序模型，准确率更高
- 比大语言模型更省算力，适合医疗场景落地
准确预测医院住院时长对提升医疗服务、资源管理和成本效率至关重要。本文提出StayLTC，一种基于液态时间常数网络（LTCs）的多模态深度学习框架，用于实时预测住院时长。LTCs利用连续时间递归动态，在结构化电子健康记录（EHR）和临床笔记数据上进行评估。在MIMIC-III数据集上的实验表明，LTCs显著优于多数时序模型，具备更高的准确性、鲁棒性及资源利用效率。此外，其在住院时长预测表现上可媲美时序大语言模型，但所需计算资源和内存大幅减少，凸显其在医疗自然语言处理任务中的应用潜力。
原文摘要 · Abstract (English)
Accurate prediction of Length of Stay (LOS) in hospitals is crucial for improving healthcare services, resource management, and cost efficiency. This paper presents StayLTC, a multimodal deep learning framework developed to forecast real-time hospital LOS using Liquid Time-Constant Networks (LTCs). LTCs, with their continuous-time recurrent dynamics, are evaluated against traditional models using structured data from Electronic Health Records (EHRs) and clinical notes. Our evaluation, conducted on the MIMIC-III dataset, demonstrated that LTCs significantly outperform most of the other time series models, offering enhanced accuracy, robustness, and efficiency in resource utilization. Additionally, LTCs demonstrate a comparable performance in LOS prediction compared to time series large language models, while requiring significantly less computational power and memory, underscoring their potential to advance Natural Language Processing (NLP) tasks in healthcare.
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