Authors: Hajung Kim, Chanhwi Kim, Jiwoong Sohn, Tim Beck, Marek Rei, Sunkyu Kim, T Ian Simpson, Joram M Posma, Antoine Lain, Mujeen Sung, Jaewoo Kang
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.
Source: http://arxiv.org/abs/2501.09744v1
