HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition

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.

GapDream: Active Synthetic Evidence Acquisition for Long-Tailed Medical Imaging
J. Chen, W. Chen
bioRxiv21 Sep 2026PreprintCC BY-NCFull textSystems & synthetic biologybioRxiv
Prevalence, acquisition, and risk factors of carbapenem-resistant enterobacterales colonization in long-term care hospitals: A prospective active surveillance study
Hyejin Hwang, Sunjoo Kim
PLOS One28 Sep 2026Peer-reviewedCC BY 4.0Full textMicrobiology Epidemiology & public healthPLOS
Maize microbiome assembly and engineering: implications for nutrient acquisition, stress resilience, and disease suppression
Thomas Argyarich Jefferson, Mehrdad Alizadeh, Dedat Prismantoro +6
Archives of Agronomy and Soil Science28 May 2026Peer-reviewedCC BY5 citedPlant biologyOpenAlex
Molecular Diagnostic Yield and Genotype-Phenotype Spectrum of Inherited Eye Diseases in a Tertiary Health Center in Türkiye.
Muhammer Özgür Çevik
Beyoglu eye journal1 Oct 2026Peer-reviewedCC BY-NCFull textGenetics & genomicsEurope PMC
Deciphering Vascular Smooth Muscle Cell Phenotype Switching via Integrated Single-Cell Transcriptomics and Machine Learning: CX3CR1 and COL1A1 as Novel Diagnostic Biomarkers for Atherosclerosis
Zhiqiang Wang, Zhenyu Zhao, Zhixi Hu +1
Metabolites1 Oct 2026Peer-reviewedCC BY 4.0ImmunologyCrossref

Cite

BibTeX RIS

BibTeX

@misc{zaripova2026hpoquest,
  title = {{HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition}},
  author = {Kamilia Zaripova and Nassir Navab and Azade Farshad and Annalisa Marsico},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.18431},
  eprint = {2609.18431},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.18431},
}

RIS

TY  - UNPB
TI  - HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition
AU  - Kamilia Zaripova
AU  - Nassir Navab
AU  - Azade Farshad
AU  - Annalisa Marsico
PY  - 2026
DA  - 2026/09/16
PB  - arXiv
DO  - 10.48550/arxiv.2609.18431
UR  - https://arxiv.org/abs/2609.18431
AB  - 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.
ER  -

Abstract

Related

Cite