Predicting 30-day hospital readmissions using ClinicalT5 with structured and unstructured electronic health records

Hospital readmission prediction is a crucial area of research due to its impact on healthcare expenditure, patient care quality, and policy formulation. Accurate prediction of patient readmissions within 30 days post-discharge remains a considerable challenging, given the complexity of healthcare data, which includes both structured (e.g., demographic, clinical) and unstructured (e.g., clinical notes, medical images) data. Consequently, there is an increasing need for hybrid approaches that effectively integrate these two data types to enhance all-cause readmission prediction performance. Despite notable advancements in machine learning, existing predictive models often struggle to achieve both high precision and balanced predictions, mainly due to the variability in patients' outcome and the complex factors influencing readmissions. This study seeks to address these challenges by developing a hybrid predictive model that combines structured data with unstructured text representations derived from ClinicalT5, a transformer-based large language model. The performance of these hybrid models is evaluated against text-only models, such as PubMedBERT, using multiple metrics including accuracy, precision, recall, and AUROC score. The results demonstrate that the hybrid models, which integrate both structured and unstructured data, outperform text-only models trained on the same dataset. Specifically, hybrid models achieve higher precision and balanced recall, reducing false positives and providing more reliable predictions. This research underscores the potential of hybrid data integration, using ClinicalT5, to improve hospital readmission prediction, thereby improving healthcare outcomes through more accurate predictions that can support better clinical decision making and reduce unnecessary readmissions.