Application of textual representation methods for clinical numerical data in early sepsis diagnosis - Zhang, Weimin; Zhou, Luyao; Shao, Min; Wang, Cui; Wang, Yu (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: sepsis diagnosis; text representation; pre-trained language models; machine learning