@inproceedings{liu-nguyen-2023-enhancing,
    title = "Enhancing Bacterial Infection Prediction in Critically Ill Patients by Integrating Clinical Text",
    author = "Liu, Jinghui  and
      Nguyen, Anthony",
    editor = "Muresan, Smaranda  and
      Chen, Vivian  and
      Casey, Kennington  and
      David, Vandyke  and
      Nina, Dethlefs  and
      Koji, Inoue  and
      Erik, Ekstedt  and
      Stefan, Ultes",
    booktitle = "Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association",
    month = nov,
    year = "2023",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.alta-1.13/",
    pages = "118--124",
    abstract = "Bacterial infection (BI) is an important clinical condition and is related to many diseases that are difficult to treat. Early prediction of BI can lead to better treatment and appropriate use of antimicrobial medications. In this paper, we study a variety of NLP models to predict BI for critically ill patients and compare them with a strong baseline based on clinical measurements. We find that choosing the proper text-based model to combine with measurements can lead to substantial improvements. Our results show the value of clinical text in predicting and managing BI. We also find that the NLP model developed using patients with BI can be transferred to the more general patient cohort for patient risk prediction."
}