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.- Anthology ID:
- 2023.alta-1.13
- Volume:
- Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association
- Month:
- November
- Year:
- 2023
- Address:
- Melbourne, Australia
- Editors:
- Smaranda Muresan, Vivian Chen, Kennington Casey, Vandyke David, Dethlefs Nina, Inoue Koji, Ekstedt Erik, Ultes Stefan
- Venue:
- ALTA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 118–124
- Language:
- URL:
- https://aclanthology.org/2023.alta-1.13
- DOI:
- Cite (ACL):
- Jinghui Liu and Anthony Nguyen. 2023. Enhancing Bacterial Infection Prediction in Critically Ill Patients by Integrating Clinical Text. In Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association, pages 118–124, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Bacterial Infection Prediction in Critically Ill Patients by Integrating Clinical Text (Liu & Nguyen, ALTA 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.alta-1.13.pdf