Abstract
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.
Abstract (translated)
表型分析对于罕见疾病的诊断至关重要，但手动从临床记录中整理结构化表型既耗时又难以扩大规模。现有的人工智能方法通常优化了表型分析的单个组件，但却没有实现完整的临床工作流程，即从临床文本中提取特征、将其标准化为人类表型本体（Human Phenotype Ontology, HPO）术语，并优先考虑具有诊断信息价值的HPO术语。我们开发了一种名为RARE-PHENIX的端到端人工智能框架，用于罕见疾病的表型分析，该框架整合了基于大型语言模型的表型提取、以本体论为基础的标准转换至HPO术语以及监督排名的诊断相关信息丰富的表型。 我们在来自11个未确诊疾病网络临床站点共计2,671名患者的资料上训练了RARE-PHENIX，并在范德比尔特大学医学中心收集到的真实世界临床记录（共16,357份）中对其进行了外部验证。使用由医生整理的HPO术语作为金标准，与最先进的深度学习基线方法（PhenoBERT）相比，在端到端评估中的本体论相似性和精确率-召回率-F1指标上，RARE-PHENIX表现更优（即基于本体论的相似性为0.70 vs 0.58）。消融分析表明，随着每个模块在RARE-PHENIX中的增加（提取、标准化和优先级），性能有所提升，这证明了模拟完整临床表型工作流程的价值。 通过将表型作为与临床对齐的工作流程进行建模，而不是单个的提取任务，RARE-PHENIX提供了结构化且经过排名的表型，这些表型更符合医生的整理，并有可能在现实世界场景中支持罕见疾病的人机协作诊断。
URL
https://arxiv.org/abs/2602.20324
