HPOQuest：一种使用主动表型采集的罕见病诊断智能体

中文摘要 AI 辅助

全球有超过3亿人受到7000多种已知罕见病中的一种影响，然而诊断仍然困难，因为患者最初呈现的表型不完整且异质。我们提出了HPOQuest，一个无需训练的框架，用于罕见病诊断中的顺序表型采集。从一小部分观察到的患者表型开始，HPOQuest维持一个概率性疾病排名，并迭代选择信息丰富的后续问题，以在患者评估期间支持临床医生。确认的表型更新疾病排名，而所有响应更新候选问题集。在四个基准队列中，HPOQuest从稀疏的初始表型中显著改善了诊断，在Recall@1上提升高达30个百分点，在Recall@5上提升高达45个百分点。这些结果表明，顺序表型采集可以从有限的初始临床证据中显著改善罕见病诊断。

英文摘要

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 training-free framework for sequential phenotype acquisition in rare-disease diagnosis. Starting from a small set of observed patient phenotypes, HPOQuest maintains a probabilistic disease ranking and iteratively selects informative follow-up questions to support clinicians during patient assessment. Confirmed phenotypes update the disease ranking, while all responses update the candidate question set. Across four benchmark cohorts, HPOQuest substantially improves diagnosis from sparse initial phenotypes, with gains of up to 30% points at Recall@1 and 45% points at Recall@5. These results demonstrate that sequential phenotype acquisition can substantially improve rare-disease diagnosis from limited initial clinical evidence.

发表机构

• Technical University of Munich（慕尼黑工业大学）
• Helmholtz Center Munich（亥姆霍兹慕尼黑中心）
• Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
• Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）
• Aalto University（阿尔托大学）

机构由 AI 辅助整理，请以论文原文为准。