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The Future of Patient Care: Text Mining Discharge Notes to Slash Readmissions
- Hospital readmission is a concerning issue affecting patient outcomes and healthcare costs.
- This study focuses on predicting patient readmission within 30 days using text mining on discharge notes.
- Machine learning and deep learning methods, including Bio-Discharge Summary Bert (BDSS), were utilized in the model.
- The model combining BDSS with a multilayer perceptron (MLP) outperformed existing methods with a 94% recall rate and 75% AUC.
- Integration of text mining and deep learning improves patient outcomes and resource allocation in healthcare.
- Utilization of EHR for readmission rate monitoring is crucial for enhancing treatment quality and cost savings in healthcare.
- Text mining and AI predictive approaches play a significant role in preventing rapid readmissions to hospitals.
- Various machine learning and deep learning models were employed to predict patient readmission based on clinical notes.
- The study compared different models, utilized advanced text representation techniques, and analyzed the entire dataset without data balancing.
- The research contributes to enhancing predictive modeling in healthcare by leveraging text mining and deep learning techniques.
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