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English(EN) CRS-Triage: Confidence- and Reliability-Aware Selective Triage under Incomplete Clinical Evidence

新型机器学习模型在临床数据不完整的情况下改进急诊分诊

研究人员开发了CRS-Triage,这是一种新颖的机器学习方法,旨在提高急诊室分诊的准确性,尤其是在电子健康记录数据不完整或不可靠时。该系统通过评估结构化数据和临床文本的可靠性以及它们之间的一致性来估计其预测的置信度得分。CRS-Triage可以有选择地推迟其置信度低的病例,并通过略微高估病情严重程度来优先避免漏诊。在MIMIC-IV-ED数据集上的实验表明,与现有方法相比,CRS-Triage提供了更好的风险覆盖权衡。 AI

影响 在数据不完美的关键决策场景中提高了AI的可靠性。

排序理由 该条目描述了一篇详细介绍用于特定应用的新型机器学习模型的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

新型机器学习模型在临床数据不完整的情况下改进急诊分诊

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该条目描述了一篇详细介绍用于特定应用的新型机器学习模型的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Guan Qiang, Yushen Chen, Tianlong Liu, David Rotenberg, Ethan H. Kim, Fang Fang ·

    CRS-Triage:不确定临床证据下的置信度和可靠性感知选择性分诊

    arXiv:2608.03862v1 Announce Type: new Abstract: Emergency triage requires reliable decisions within a short time period. However, the available electronic health record (EHR) data, including structured data and clinical text, are often incomplete, unreliable, and inconsistent. Th…