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Predicting ICU Readmission from Electronic Health Records via BERTopic with Long Short Term Memory Network Approach.

Chih-Chou Chiu¹, Chung-Min Wu¹, Te-Nien Chien²

¹Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan.

Journal of Clinical Medicine

|September 28, 2024

Summary

This summary is machine-generated.

This study introduces a hybrid model combining BERTopic and Long Short-Term Memory (LSTM) networks to predict intensive care unit (ICU) readmissions, achieving an AUROC of 0.80 by effectively using text data from electronic health records.

Keywords:

BERTopic ICU readmission LSTM network deep learning electronic health records healthcare

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Clinical Data Science

Background:

Intensive care unit (ICU) readmissions present significant healthcare challenges, impacting patient outcomes and costs.
Predicting ICU readmissions is vital for enhancing medical quality and reducing healthcare expenditures.
Traditional analyses of electronic health records (EHRs) often overlook valuable unstructured text data.

Purpose of the Study:

To develop and evaluate a hybrid model for predicting ICU readmissions.
To integrate unstructured text data from EHRs into predictive models.
To improve the accuracy and interpretability of ICU readmission predictions.

Main Methods:

A hybrid model combining BERTopic (unsupervised topic modeling) and Long Short-Term Memory (LSTM) networks (deep learning) was developed.
The model utilized both quantitative and text data from the MIMIC-III database, transforming discharge summaries into topic vectors.
Unsupervised topic modeling (BERTopic) extracted themes from patient records to guide feature selection for supervised learning.

Main Results:

The hybrid model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.80, outperforming traditional machine learning models.
BERTopic effectively leveraged unstructured EHR data, identifying themes that improved readmission prediction accuracy and interpretability.
Variable importance ranking provided insights into the interplay of factors influencing readmission predictions.

Conclusions:

The hybrid BERTopic-LSTM model offers a powerful approach for predicting ICU readmissions by integrating unstructured text data.
This methodology enhances patient management, aids in developing personalized treatment strategies, and supports resource optimization.
The study underscores the value of combining diverse data types for more accurate, interpretable, and cost-effective healthcare predictive models.