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

ABSTRACT: 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

SUBMITTER: Pandey SR 

PROVIDER: S-EPMC12404500 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature