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English(EN) Teaching LLMs to Recommend and Defer in Underrepresented Epilepsy Care

大型语言模型框架MANANA改善了资源匮乏环境下的癫痫护理

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为MANANA的新框架，以帮助大型语言模型（LLMs）在代表性不足的癫痫护理环境中协助临床医生。这个非参数提示学习框架通过学习少量患者数据来适应当地的处方实践。MANANA提高了处方准确性，并提供基于不确定性的推迟信号，使系统能够处理确定的病例，并将不太确定的病例推迟给专家。 AI

影响 这项研究展示了如何将大型语言模型应用于资源受限环境中的专业医疗决策支持，从而可能改善护理的可及性。

排序理由 该集群包含一篇详细介绍大型语言模型新框架的学术论文。[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 →

大型语言模型框架MANANA改善了资源匮乏环境下的癫痫护理

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该集群包含一篇详细介绍大型语言模型新框架的学术论文。[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 English(EN) · Shreyas Rajesh, Kartik Sharma, Tonmoy Monsoor, Mehmet Yigit Turali, Richard Idro, Juliana Kayaga, Robert Sebunya, Tracy Tushabe Namata, Jessica Nichole Pasqua, Vwani Roychowdhury, Rajarshi Mazumder ·

    教会大型语言模型在代表性不足的癫痫护理中进行推荐和推迟

    arXiv:2606.31036v1 Announce Type: new Abstract: Specialist epilepsy expertise is scarce in resource-constrained settings, making LLM-based decision support attractive for frontline clinicians managing longitudinal treatment. Such systems must adapt to local prescribing practice a…

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