# An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

arXiv: https://arxiv.org/abs/2602.20324 · HTML: https://mlsanity.com/paper/2602.20324/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 69 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 18 of 18)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [An Agentic System for Rare Disease Diagnosis with Traceable Reasoning](https://mlsanity.com/paper/2506.20430/) (2025)
- [Identifying and Extracting Rare Disease Phenotypes with Large Language Models](https://mlsanity.com/paper/2306.12656/) (2023)
- [Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT](https://mlsanity.com/paper/2308.06294/) (2023)
- [The RareDis corpus: a corpus annotated with rare diseases, their signs and symptoms](https://mlsanity.com/paper/2108.01204/) (2021)
- [Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping](https://mlsanity.com/paper/2312.06457/) (2023)
- [PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology](https://mlsanity.com/paper/2009.08478/) (2020)
- [An evaluation of GPT models for phenotype concept recognition](https://mlsanity.com/paper/2309.17169/) (2023)
- [Information Extraction from Clinical Notes: Are We Ready to Switch to Large Language Models?](https://mlsanity.com/paper/2411.10020/) (2024)
- [Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts](https://mlsanity.com/paper/2109.00343/) (2021)
- [Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes](https://mlsanity.com/paper/2205.05656/) (2022)
- [A Hybrid Framework with Large Language Models for Rare Disease Phenotyping](https://mlsanity.com/paper/2405.10440/) (2024)
- [Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes](https://mlsanity.com/paper/2503.12286/) (2025)
- [A Specialized Large Language Model for Clinical Reasoning and Diagnosis in Rare Diseases](https://mlsanity.com/paper/2511.14638/) (2025)
- [A Weakly Supervised Transformer for Rare Disease Diagnosis and Subphenotyping from EHRs with Pulmonary Case Studies](https://mlsanity.com/paper/2507.02998/) (2025)

## Abstract (arXiv)

Phenotyping is fundamental to rare disease diagnosis, but manual curation of structured phenotypes from clinical notes is labor-intensive and difficult to scale. Existing artificial intelligence approaches typically optimize individual components of phenotyping but do not operationalize the full clinical workflow of extracting features from clinical text, standardizing them to Human Phenotype Ontology (HPO) terms, and prioritizing diagnostically informative HPO terms. We developed RARE-PHENIX, an end-to-end AI framework for rare disease phenotyping that integrates large language model-based phenotype extraction, ontology-grounded standardization to HPO terms, and supervised ranking of diagnostically informative phenotypes. We trained RARE-PHENIX using data from 2,671 patients across 11 Undiagnosed Diseases Network clinical sites, and externally validated it on 16,357 real-world clinical notes from Vanderbilt University Medical Center. Using clinician-curated HPO terms as the gold standard, RARE-PHENIX consistently outperformed a state-of-the-art deep learning baseline (PhenoBERT) across ontology-based similarity and precision-recall-F1 metrics in end-to-end evaluation (i.e., ontology-based similarity of 0.70 vs. 0.58). Ablation analyses demonstrated performance improvements with the addition of each module in RARE-PHENIX (extraction, standardization, and prioritization), supporting the value of modeling the full clinical phenotyping workflow. By modeling phenotyping as a clinically aligned workflow rather than a single extraction task, RARE-PHENIX provides structured, ranked phenotypes that are more concordant with clinician curation and has the potential to support human-in-the-loop rare disease diagnosis in real-world settings.