Explainable NLP model for predicting patient admissions at emergency department using triage notes

Arnaud, Emilien; Elbattah, Mahmoud; Moreno-Sánchez, Pedro A.; Dequen, Gilles; Ghazali, Daniel Aiham

Abstract

Explainable Artificial Intelligence (XAI) has the potential to revolutionize healthcare by providing more transparent, trustworthy, and understandable predictions made by AI models. To this end, the present study aims to develop an explainable NLP model for predicting patient admissions to the emergency department based on triage notes. We utilize transformer models to leverage the extensive textual data captured in triage notes, while also delivering interpretable results by using the LIME approach. The results show that the proposed model provides satisfactory accuracy along with an interpretable understanding of the factors contributing to patient admission. In general, this work highlights the potential of NLP in improving patient care and decision-making in emergency medicine.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2023 IEEE International Conference on Big Data (BigData)
• Acceptance Date: Oct 15, 2023
• Online Publication Date: Jan 22, 2024
• Publication Date: Jan 22, 2024
• Deposit Date: Jan 23, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Pages: 4843-4847
• Book Title: 2023 IEEE International Conference on Big Data (BigData)
• ISBN: 9798350324464
• DOI: https://doi.org/10.1109/bigdata59044.2023.10386753
• Public URL: https://uwe-repository.worktribe.com/output/11624342