Kumar, Akshi; Malla, Jyothi and Sharma, Aditi. 2025. BioBERT-RxReadmit: Improving Hospital Readmission Predictions Through Clinical Text Analysis with BioBERT. International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies, 2(1), pp. 14-29. ISSN 3048-8788 [Article]

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Abstract or Description

This study introduces BioBERT-RxReadmit, a dual stage model designed to predict hospital readmissions using unstructured clinical data from the MIMIC-III dataset. The model's name reflects its dual focus: leveraging BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Texts) for analyzing medical text and Rx, symbolizing prescription and medical intervention, to address readmission risks. In the first stage, BioBERT identifies key clinical features such as symptoms, diagnoses, and treatments from free-text clinical notes. These features are then integrated with the complete clinical text in the second stage, where BioBERT is fine-tuned for classification to predict 30-day readmissions. This comprehensive approach improves the model's ability to recognize complex patterns in patient data, resulting in improved predictive accuracy. BioBERT-RxReadmit helps identify high-risk patients more effectively, reducing preventable readmissions, optimizing healthcare resources, and improving patient care, showcasing the transformative potential of advanced NLP mod-els in healthcare.

Item Type:	Article
Data Access Statement:	This study utilizes the publicly available MIMIC-III dataset, which is a critical care database provided by the Massachusetts Institute of Technology (MIT). The dataset is accessible to researchers who complete the required data usage training and obtain necessary approvals via the PhysioNet platform (https://physionet.org/content/mimiciii/1.4/). No additional datasets were generated or analyzed during this study.
Keywords:	BioBERT, Hospital Readmissions, Clinical Documentation, MIMIC-III Dataset, Predictive Modelling, Healthcare AI, Natural Language Processing
Departments, Centres and Research Units:	Computing
Dates:	Date Event 28 January 2025 Accepted 31 January 2025 Published Online
Item ID:	38253
Date Deposited:	03 Feb 2025 13:23
Last Modified:	03 Feb 2025 13:25
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/38253

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