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Română(RO) ARMD-Predict: multi-site machine learning to predict antibiotic resistance before culture results

机器学习模型在培养结果出来前预测抗生素耐药性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了ARMD-Predict，这是一个多中心机器学习模型，旨在在传统培养结果可用之前预测患者的抗生素耐药性。该模型在四个学术医疗中心的数百万次观察数据上进行了训练，在预测个体抗生素敏感性和识别感染生物方面显示出有希望的准确性。尽管显示出指导经验性抗生素选择的潜力，研究人员强调在临床实施之前需要进行独立的校准和前瞻性验证。 AI

影响 通过实现更快、数据驱动的抗生素选择，有可能改善临床决策，从而减少耐药性的传播。

排序理由 该集群包含一篇详细介绍用于特定科学应用的新机器学习模型的学术论文。[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 →

机器学习模型在培养结果出来前预测抗生素耐药性

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该集群包含一篇详细介绍用于特定科学应用的新机器学习模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 Română(RO) · Fateme Nateghi Haredasht, Nicholas P. Marshall, Wenyuan Chen, Lauren N. Cooper, Ziming Wei, Harrison Konsker, Jacob S. Chen, Michael Klompas, Cara McKenna, Richard J. Medford, Sanjat Kanjilal, Austin J. Schoeffler, Stephen P. Ma, Niaz Banaei, Steven M. A… ·

    ARMD-Predict：多位点机器学习预测抗生素耐药性，早于培养结果

    arXiv:2610.32998v1 Announce Type: cross Abstract: Clinicians select empiric antibiotics before culture susceptibility results are available. Electronic health record (EHR) models could inform that choice, but most are developed at a single hospital and transport poorly to others.…

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