AI Insight
HPOQuest is a computational framework designed to improve rare disease diagnosis by strategically selecting follow-up questions about patient symptoms and characteristics. Starting with limited initial symptom information, the system maintains a probabilistic ranking of possible diseases and iteratively identifies the most informative questions to ask, updating its disease predictions based on patient responses. Testing across four benchmark datasets showed improvements of up to 30 percentage points in top diagnosis accuracy and 45 percentage points in top-5 accuracy compared to baseline approaches.
Why it matters
With over 300 million people worldwide affected by rare diseases that are notoriously difficult to diagnose due to incomplete initial presentations, this tool could help clinicians systematically gather the most relevant information during patient assessment. The training-free approach makes it potentially deployable without requiring extensive disease-specific data collection.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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 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.
Source: HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition