Published November 12, 2023 | Version v1
Conference proceeding Open

KU AIGEN ICL EDI@BC8 Track 3: Advancing Phenotype Named Entity Recognition and Normalization for Dysmorphology Phys- ical Examination Reports

  • 1. Department of Computer Science and Engineering, Korea University
  • 2. School of Medicine, University of Nottingham
  • 3. Department of Computer Science, Imperial College London
  • 4. AIGEN Sciences
  • 5. Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh
  • 6. Department of Metabolism, Digestion and Reproduction, Imperial College London



The objective of BioCreative8 Track 3 is to extract phenotypic key medical findings embedded within EHR texts and subsequently normalize these findings to their Human Phenotype Ontology (HPO) terms. However, the presence of diverse surface forms in phenotypic findings makes it challenging to accurately normalize them to the correct HPO terms. To address this challenge, we explored various models for named entity recognition and implemented data augmentation techniques such as synonym marginalization to enhance the normalization step. Our pipeline resulted in an exact extraction and normalization F1 score 2.6% higher than the mean score of all submissions received in response to the challenge. Furthermore, in terms of the normalization F1 score, our approach surpassed the average performance by 1.9%. These findings contribute to the advancement of automated medical data extraction and normalization techniques, showcasing potential pathways for future research and application in the biomedical domain.


This article is part of the Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models.



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Conference proceeding: 10.5281/zenodo.10103190 (DOI)