Enhancing Readmission Prediction with Deep Learning: Extracting Biomedical Concepts from Clinical Texts

TL;DR

This paper presents a deep learning approach utilizing biomedical concept extraction from clinical texts to predict hospital readmissions within 30 days, demonstrating improved accuracy over existing methods.

Contribution

It introduces a novel combination of the BDSS model with PCA and deep learning for readmission prediction, advancing healthcare predictive analytics.

Findings

Achieved 94% recall in readmission prediction

Attained 75% AUC indicating strong model performance

Outperformed existing state-of-the-art methods

Abstract

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.