Electronic Health Data Records for Diabetes Patients Based on Deep Learning Models: A Review

The use of deep learning models for analyzing electronic health records (EHR) data in diabetes management has grown significantly. Researchers are leveraging deep learning technologies to enhance the diagnosis, treatment, and management of diabetes mellitus by extracting valuable insights from EHR data. Various deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been evaluated for their effectiveness in handling EHR data and predicting clinical outcomes. CNNs excel at processing spatial data, while RNNs are adept at managing sequential data, although both have limitations. Advanced models like autoencoders (AEs) and deep belief networks (DBNs) offer improvements in feature extraction and predictive accuracy. Hybrid and ensemble techniques also show promise in enhancing performance. Despite these advancements, challenges such as data availability, model interpretability, and generalizability remain. Ongoing research is essential to address these issues and further improve diabetes management through EHR analysis.