论文概要
研究领域: ML 作者: Haresh Rengaraj Rajamohan, Xiang Gao, Weicheng Zhu, Shih-Lun Huang, Long Chen... 发布时间: 2026-03-25 arXiv: 2603.24562
中文摘要
虽然大规模预训练已经彻底改变了语言建模，但其在医疗领域结构化电子健康记录（EHR）中的潜力仍未得到充分探索。本文提出RAVEN，一种基于复发感知下次就诊事件预测的序列EHR数据生成式预训练新策略。利用超过一百万独立个体的数据集，我们的模型学习根据患者病史自回归地生成下次就诊的标记化临床事件。
原文摘要
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 for sequential EHR data based on Recurrence-Aware next-Visit EveNt prediction. Leveraging a dataset of over one million unique individuals, our model learns to autoregressively generate tokenized clinical events for the next visit conditioned on patient history.
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