研究人员开发了几种利用电子健康记录（EHR）上的机器学习来改进临床预测的新方法。其中一种方法“Risk Horizons”使用一种具有几何感知的框架和双曲嵌入来构建患者特定的候选空间，以预测未来的临床事件。另一种方法将临床诊断视为一个自回归序列建模任务，采用大型语言模型的因果解码器来处理缺失的模态并提高可解释性。此外，一个名为FlatASCEND的新模型专注于具有连续时间预测的自回归临床序列生成，并测试药理学关联，而另一项研究则使用稀疏自编码器来分解此类临床序列模型的表征。 AI
影响 这些进展可能带来更准确、更具可解释性的由人工智能驱动的医疗诊断工具和治疗规划。
排序理由 多篇arXiv论文展示了使用机器学习技术在临床预测和序列建模方面的新研究。
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arXiv:2602.12828v2 Announce Type: replace Abstract: Predicting future clinical events from longitudinal electronic health records (EHRs) requires selecting plausible outcomes from a large and structured event space under sparse observations. While clinical coding systems provide …
arXiv:2604.18753v2 Announce Type: replace Abstract: An active challenge in developing multimodal machine learning (ML) models for healthcare is handling missing modalities during training and deployment. As clinical datasets are inherently temporal and sparse in terms of modality…
arXiv:2605.04071v1 Announce Type: new Abstract: Autoregressive models can predict clinical events, but generating patient-conditioned multi-step trajectories that respond to intervention tokens and testing whether those responses preserve known pharmacological associations has re…
arXiv:2605.04072v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have been applied to large language models and protein language models, but not systematically to electronic health record (EHR) foundation models. We train TopK SAEs on FlatASCEND, a 14.5-million-paramete…
Multimodal clinical records contain structured measurements and clinical notes recorded over time, offering rich temporal information about the evolution of patient health. Yet these observations are sparse, and whether they are recorded depends on the patient's latent condition.…