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Paper details
Number 4 - December 2024
Volume 34 - 2024
Application of textual representation methods for clinical numerical data in early sepsis diagnosis
Weimin Zhang, Luyao Zhou, Min Shao, Cui Wang, Yu Wang
Abstract
Sepsis is a severe infectious disease with high incidence and mortality rates worldwide. Early diagnosis of sepsis in newly
admitted intensive care unit patients is crucial to reduce mortality and improve patient outcomes. The manual diagnostic
methods heavily rely on subjective clinical experience, while traditional machine learning methods require time-consuming
feature engineering and the performance is limited by the knowledge acquired from scarce datasets. Therefore, to address
the aforementioned issues, this study proposes a novel textual representation method for clinical numerical data, leveraging
pre-trained language models from the field of natural language processing for sepsis prediction. Specifically, this study
innovatively transforms structured clinical numerical data of patients into unstructured textual descriptions. This transformation reframes sepsis prediction into a text classification task, leveraging the rich prior semantic knowledge embedded in pre-trained language models to enhance prediction performance. The proposed method is validated using real ICU clinical data. When employing RoBERTa-base, it achieved an F1 score of 79.03%, which represents an improvement of five percentage points compared with commonly used machine learning classifiers. The experiments confirmed that the proposed method enhances the performance of early sepsis diagnosis and introduces new insights for clinical diagnosis of sepsis.
Keywords
sepsis diagnosis, text representation, pre-trained language models, machine learning