Authors:
(1) Rasoul Samani, School of Electrical and Computer Engineering, Isfahan University of Technology and this author contributed equally to this work;
(2) Mohammad Dehghani, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran and this author contributed equally to this work ([email protected]);
(3) Fahime Shahrokh, School of Electrical and Computer Engineering, Isfahan University of Technology.
Table of Links
4. Evaluation
5. Conclusion
Medical data, particularly EHR data, presents a rich source for text mining studies. These studies hold promise in various healthcare applications. Reducing ICU readmission rates is paramount for hospitals to enhance patient outcomes, conserve ICU resources, and curtail healthcare expenses. In this study, we aimed to leverage patient discharge reports, which offer detailed insights into a patient's medical history, current condition, and treatment recommendations, to develop a predictive model for ICU readmission. Our proposed deep learning-based model demonstrated superior performance compared to traditional machine learning models, achieving higher AUC. For future research, exploring alternative deep learning architectures beyond MLP could be beneficial. Additionally, Large Language Models (LLM) can be considered for creating predictive models and conducting comparative analyses with deep learning models. To enhance their effectiveness, we recommend considering the use of larger input data and leveraging advanced models like the LongFormer. Additionally, incorporating summarization techniques during the pre-processing stage can further improve the quality of input data.
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