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

TL;DR

This paper presents a new pipeline for extracting and normalizing phenotypic findings from medical reports, improving accuracy in mapping to Human Phenotype Ontology terms through models and data augmentation.

Contribution

The study introduces a novel approach combining models and data augmentation techniques to enhance phenotype entity recognition and normalization in clinical texts.

Findings

Exact extraction and normalization F1 score improved by 2.6%.

Normalization F1 score surpassed average by 1.9%.

Demonstrates potential for automated biomedical data processing.

Abstract

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.