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临床指导模型还是基础模型? 从电子健康记录中预测宫脊髓炎骨髓病

Salim Yakdan1, Ben Warner2, Zoher Ghogawala3

  • 1Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.

NPJ digital medicine
|January 20, 2026
PubMed

颈椎病性脊髓病（CSM）是老年人群中脊髓功能障碍的首要原因，但由于症状隐匿性起病且临床认知有限，诊断常被延误。我们利用结构化的电子健康记录（EHR）数据开发并进行了外部验证，构建了可提前长达30个月预测新发CSM诊断的机器学习模型。基于Merative™ MarketScan®保险理赔数据库及本机构EHR中约200万名患者的数据，我们评估了一系列建模策略，范围涵盖从简单的临床指导型架构到大规模预训练基础模型。具体包括基于计数的前馈网络、经临床筛选优化的Mamba状态空间模型、两种中等规模的Transformer模型（CoreBEHRT和CEHRBERT），以及大型基础模型（clmbr-t-base和clmbr-t-5k-CSM）。尽管大型基础模型在更大、更具异质性的数据集内部验证中整体表现更优，但在跨独立医疗系统的外部验证中，面向临床领域的模型展现出更强的泛化能力。这些结果凸显了基础模型在捕捉丰富EHR表征方面的潜力，同时也揭示了其在可推广性方面仍存在的持续挑战。相比之下，尽管领域知识驱动的模型结构较为简单，但在不同医疗环境中的稳健性可能更高。 关键词：颈椎病性脊髓病，电子健康记录，机器学习，基础模型，Transformer，Mamba状态空间模型，临床预测，外部验证

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