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

Hamid, Dalal Nawfl; Younis, Mohammed Chachan

doi:10.33899/csmj.2024.148886.1115

	Electronic Health Data Records for Diabetes Patients Based on Deep Learning Models: A Review
AL-Rafidain Journal of Computer Sciences and Mathematics
Article 7, Volume 18, Issue 2, December 2024, Pages 52-64 PDF (10.43 M)
Document Type: Review Paper
DOI: 10.33899/csmj.2024.148886.1115
Authors
Dalal Nawfl Hamid^* ¹; Mohammed Chachan Younis²
¹University of Mosul / College of Computer Sciences and Mathematics / Department of Computer Sciences
²University of Mosul - College of Computer Sciences and Mathematics – Department of Computer Sciences
Abstract
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.
Keywords
Deep Learning; Diabetes; Electronic Health Records; Recurrent Neural Networks; Convolutional Neural Networks

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