Learning to Fuse LLMs with Ontology Rankers for Rare-Disease Diagnosis
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TL;DR - A learned fusion model combines LLM-generated differential diagnoses with an ontology ranker, improving rare-disease diagnosis while preserving inspectable phenotype-based evidence. It also addresses a documented test-set leakage pathway before evaluation.
- Fusion improved Phenomizer Recall@1 by 7.86 percentage points on Phenopacket Store and 20.18 points on RAMEDIS.
- With DeepSeek-V4-Flash, a fusion model trained on other LLMs raised Recall@1 from 0.1657 to 0.2176 without retraining.
- The model dynamically weighs ranked-list agreement and ontology support for each case.
- Candidate-level ontology evidence remained available for 90.8% of correct fused diagnoses.
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Learning to Fuse LLMs with Ontology Rankers for Rare-Disease Diagnosis
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TL;DR - A learned fusion model combines LLM-generated differential diagnoses with an ontology ranker, improving rare-disease diagnosis while preserving inspectable phenotype-based evidence. It also addresses a documented test-set leakage pathway before evaluation.
- Fusion improved Phenomizer Recall@1 by 7.86 percentage points on Phenopacket Store and 20.18 points on RAMEDIS.
- With DeepSeek-V4-Flash, a fusion model trained on other LLMs raised Recall@1 from 0.1657 to 0.2176 without retraining.
- The model dynamically weighs ranked-list agreement and ontology support for each case.
- Candidate-level ontology evidence remained available for 90.8% of correct fused diagnoses.