An Integrated Approach to Address Class Imbalance in Medical Text Classification via Deep Learning and Specialized Embeddings

The objective of this study was to leverage the capabilities of deep learning for the classification of medical texts, culminating in the development of the DeepAtt-MCNN-BiLSTM model. This innovative model integrates multi-scale convolutional neural networks, advanced attention mechanisms, and bidirectional LSTMs to adeptly decipher complex clinical narratives. The model was subjected to stringent testing using the MIMIC-III dataset, where it consistently outperformed conventional machine learning and standalone deep learning models. Evaluation metrics such as accuracy, precision, recall, F1-Score, and AUC-ROC were employed to highlight the model's adeptness at managing the intricacies of medical data. The incorporation of domain-specific BERT embeddings significantly enhanced the model's comprehension of medical jargon. The DeepAtt-MCNN-BiLSTM model offers a combination of precision and comprehensiveness in medical forecasting, which is especially beneficial for diagnosing rare medical conditions. Future improvements could involve transfer learning, the incorporation of additional data types, and boosting the model's interpretability. This research marks a significant advancement in the field of medical text classification, underscoring the crucial role of deep learning in propelling the evolution of healthcare diagnostics.