HPOQuest improves rare disease diagnosis by asking smart questions
HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition
Artificial IntelligenceMachine Learning
Summary
Diagnosing rare diseases is hard because patients often show only some symptoms at first. The authors created HPOQuest, a tool that starts with a few known symptoms and then asks doctors the most useful follow-up questions to learn more. Each new symptom helps refine which diseases are most likely, making diagnosis more accurate. Tests showed HPOQuest can greatly improve diagnosis accuracy even when starting with limited information.
What this means in practice
- •For clinical decision support teams: Integrate a tool that guides clinicians by suggesting next best phenotype questions to improve rare disease diagnosis accuracy during patient assessments.
- •For genetic testing companies: Enhance diagnostic algorithms by incorporating sequential phenotype queries to prioritize candidate diseases from sparse patient data.$Commercial implications: Enables development of improved genetic diagnostic services by maximizing information from limited phenotypic data to deliver more accurate results.
Authors
Kamilia Zaripova, Nassir Navab, Azade Farshad, Annalisa Marsico
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.