  Hospital readmission, defined as patients being re-hospitalized shortly after
discharge, is a critical concern as it impacts patient outcomes and healthcare
costs. Identifying patients at risk of readmission allows for timely
interventions, reducing re-hospitalization rates and overall treatment costs.
This study focuses on predicting patient readmission within less than 30 days
using text mining techniques applied to discharge report texts from electronic
health records (EHR). Various machine learning and deep learning methods were
employed to develop a classification model for this purpose. A novel aspect of
this research involves leveraging the Bio-Discharge Summary Bert (BDSS) model
along with principal component analysis (PCA) feature extraction to preprocess
data for deep learning model input. Our analysis of the MIMIC-III dataset
indicates that our approach, which combines the BDSS model with a multilayer
perceptron (MLP), outperforms state-of-the-art methods. This model achieved a
recall of 94% and an area under the curve (AUC) of 75%, showcasing its
effectiveness in predicting patient readmissions. This study contributes to the
advancement of predictive modeling in healthcare by integrating text mining
techniques with deep learning algorithms to improve patient outcomes and
optimize resource allocation.
