English(EN) Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction

新的RAVEN模型扩展了EHR数据以进行临床事件预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了RAVEN，一种用于顺序电子健康记录（EHR）数据的新生成预训练策略。该模型采用具回溯意识的下一次就诊事件（Recurrence-Aware next-Visit EveNt）预测方法，并在超过一百万份患者记录上进行了训练。RAVEN能够根据患者病史生成未来就诊的临床事件，并在疾病预测方面展现出强大的零样本性能，优于Transformer模型和提示式LLM。 AI

影响该模型可以通过利用大规模EHR数据来改进临床预测和疾病预测。

排序理由发布了一篇详细介绍新模型和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Xiang Gao, Haresh Rengaraj Rajamohan, Weicheng Zhu, Shih-Lun Huang, Long Chen, Gabe Schulman, Huizhen Jin, Shengduo Li, Yixuan Wang, Huidi Yang, Kyunghyun Cho, Cem M. Deniz, Narges Razavian · 2026-11-06 05:00

通过预测下次就诊来扩展用于临床记录的具有递归意识的基础模型

arXiv:2603.24562v3 Announce Type: replace Abstract: While large-scale pretraining has revolutionized language modeling, its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present RAVEN, a novel generative pretraining strategy fo…

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通过预测下次就诊来扩展用于临床记录的具有递归意识的基础模型

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