English(EN) Routine laboratory trajectories encode the onset of organ-level complications in cancer

AI可提前两年预测癌症并发症

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-07 09:38

研究人员开发了一种Transformer模型，能够提前两年预测癌症患者器官级并发症的发生。该模型分析纵向实验室测量数据，捕捉单时间点工具所遗漏的时间生理变化。这种方法在预测多发性骨髓瘤和卵巢癌患者的162种并发症方面显示出显著的富集性，并且预测结果可迁移到独立的医疗保健系统。 AI

影响能够对癌症治疗并发症进行主动的患者监测和干预，有可能改善治疗效果并降低医疗成本。

排序理由该集群包含一篇详细介绍AI在医疗保健领域新应用的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Jannik L\"ubberstedt, Krischan Braitsch, Jacqueline Lammert, Christof Winter, Florian Gabriel, Tristan Lemke, Christopher Zirn, Markus Graf, Friedrich Puttkammer, Hartmut H\"antze, Johannes Moll, Anirudh Narayanan, Andrei Zhukov, Fabian Drexel, Zeineb Be… · 2026-06-09 04:00

常规实验室轨迹编码器官并发症在癌症中的发病

arXiv:2606.08538v1 Announce Type: new Abstract: Routine laboratory panels drawn during cancer treatment constitute longitudinal physiological recordings of organ function, yet their temporal structure is discarded by single-timepoint prognostic tools. A transformer trained on 2,7…
arXiv cs.LG TIER_1 English(EN) · Keno Bressem · 2026-06-07 09:38

常规实验室轨迹编码癌症器官并发症的发生

Routine laboratory panels drawn during cancer treatment constitute longitudinal physiological recordings of organ function, yet their temporal structure is discarded by single-timepoint prognostic tools. A transformer trained on 2,777,595 laboratory measurements from 3,905 patien…