An Improved Method for Phenotype Concept Recognition Using Rich HPO Information

Automatically identifying human phenotype ontology (HPO) concepts from text is important for disease analysis. Existing ontology-driven methods for phenotype concept recognition mainly rely on concept names and synonym information from the ontology, without fully exploiting the rich ontology information. In this paper, we present an improved phenotype concept recognition method by incorporating rich HPO information. We first design prompts with HPO information and use a cutting-edge large language model GPT-4 to generate synonym augmentation for expanding distant supervised training data. We then propose an ontology vector-enhanced phenotype concept classification model to efficiently integrate the taxonomic hierarchical structure of HPO. Additionally, we employ noisy data augmentation to improve the model’s recognition ability in noisy texts and implement a negation detection function. Experimental results on three standard corpora and two typo corpora show our method compares favorably to previous methods and achieves a significant improvement in noisy texts. The source code and data are freely available at https://github.com/DUTIR-BioNLP/PhenoTagger-Updates.