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English(EN) How Deep is the Signal? Time-Context up to 30 Years for Colorectal Cancer Prediction in a Primary-Care EHR Model

AI模型预测结直肠癌需要长达18年的电子病历数据

研究人员引入了“时间背景”的概念,以评估AI模型在电子健康记录(EHR)中进行预测信号需要回溯多长时间。一项使用Transformer模型对超过360万患者的电子病历数据进行的研究发现,结直肠癌的预测准确性随着回溯时间的延长而提高,长达18年,其中大部分收益在10年内实现。此后,模型的性能趋于平稳,这表明尽管长期历史数据至关重要,但其预测效用存在局限性。 AI

影响 引入“时间背景”作为临床预测模型的关键指标,表明电子病历基础模型需要更长的历史数据。

排序理由 该集群包含一篇详细介绍AI模型性能新方法和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI模型预测结直肠癌需要长达18年的电子病历数据

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该集群包含一篇详细介绍AI模型性能新方法和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Achille Raviez, Lan Wang, Brendan Delaney ·

    信号有多深?初级保健电子健康记录模型中结直肠癌预测长达30年的时间-背景

    arXiv:2611.07265v1 Announce Type: cross Abstract: EHR foundation models read medical events within a fixed context window, and long context has so far been measured only in tokens. How far back in time a model must look to capture the earliest predictive signal is unknown. We int…