English(EN) HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition

新AI框架HPOQuest助力罕见病诊断

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了HPOQuest，一个旨在通过主动获取患者表型来辅助罕见病诊断的新型框架。该系统无需训练，从有限的观察症状集开始，并为临床医生迭代选择信息量最大的后续问题。通过更新已确认表型的疾病排名并根据响应优化问题集，HPOQuest在基准队列中已显示出诊断准确性的显著提高，在Recall@1上的准确率提高了高达30个百分点，在Recall@5上的准确率提高了45个百分点。该框架突出了顺序表型获取在从稀疏的初始临床数据中增强罕见病诊断的潜力。 AI

影响该框架可以显著提高罕见病的诊断准确性，可能带来更早、更有效的治疗。

排序理由发表了一篇详细介绍特定应用新AI框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Kamilia Zaripova, Nassir Navab, Azade Farshad, Annalisa Marsico · 2026-09-17 04:00

HPOQuest：一种使用主动表型采集的罕见病诊断剂

arXiv:2609.18431v1 Announce Type: new Abstract: More than 300 million people worldwide are affected by one of over 7,000 known rare diseases, yet diagnosis remains difficult because patients initially present with incomplete and heterogeneous phenotypes. We present HPOQuest, a tr…

本文如何被排名

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HPOQuest：一种使用主动表型采集的罕见病诊断剂

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