Multi-objective Representation for Numbers in Clinical Narratives Using CamemBERT-bio

• URL: http://arxiv.org/abs/2405.18448v2
• Date: Wed, 10 Jul 2024 08:47:52 GMT
• Title: Multi-objective Representation for Numbers in Clinical Narratives Using CamemBERT-bio
• Authors: Boammani Aser Lompo, Thanh-Dung Le,
• Abstract summary: This research aims to classify numerical values extracted from medical documents across seven physiological categories. We introduce two main innovations: integrating keyword embeddings into the model and adopting a number-agnostic strategy. We show substantial improvements in the effectiveness of CamemBERT-bio, surpassing conventional methods with an F1 score of 0.89.
• Score: 0.9208007322096533
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This research aims to classify numerical values extracted from medical documents across seven distinct physiological categories, employing CamemBERT-bio. Previous studies suggested that transformer-based models might not perform as well as traditional NLP models in such tasks. To enhance CamemBERT-bio's performances, we introduce two main innovations: integrating keyword embeddings into the model and adopting a number-agnostic strategy by excluding all numerical data from the text. The implementation of label embedding techniques refines the attention mechanisms, while the technique of using a `numerical-blind' dataset aims to bolster context-centric learning. Another key component of our research is determining the criticality of extracted numerical data. To achieve this, we utilized a simple approach that involves verifying if the value falls within the established standard ranges. Our findings are encouraging, showing substantial improvements in the effectiveness of CamemBERT-bio, surpassing conventional methods with an F1 score of 0.89. This represents an over 20\% increase over the 0.73 $F_1$ score of traditional approaches and an over 9\% increase over the 0.82 $F_1$ score of state-of-the-art approaches. All this was achieved despite using small and imbalanced training datasets.

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