PT: A Plain Transformer is Good Hospital Readmission Predictor
Journal:
arXiv
Published Date:
Dec 17, 2024
Abstract
Hospital readmission prediction is critical for clinical decision support,
aiming to identify patients at risk of returning within 30 days post-discharge.
High readmission rates often indicate inadequate treatment or post-discharge
care, making effective prediction models essential for optimizing resources and
improving patient outcomes. We propose PT, a Transformer-based model that
integrates Electronic Health Records (EHR), medical images, and clinical notes
to predict 30-day all-cause hospital readmissions. PT extracts features from
raw data and uses specialized Transformer blocks tailored to the data's
complexity. Enhanced with Random Forest for EHR feature selection and test-time
ensemble techniques, PT achieves superior accuracy, scalability, and
robustness. It performs well even when temporal information is missing. Our
main contributions are: (1)Simplicity: A powerful and efficient baseline model
outperforming existing ones in prediction accuracy; (2)Scalability: Flexible
handling of various features from different modalities, achieving high
performance with just clinical notes or EHR data; (3)Robustness: Strong
predictive performance even with missing or unclear temporal data.
