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 d...
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| Format: | Article |
| Language: | English |
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Mosul University
2024-12-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
| Subjects: | |
| Online Access: | https://csmj.uomosul.edu.iq/article_185890_2df03c57a8b26bd238a5e3759e9c1a91.pdf |
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| _version_ | 1850175097192579072 |
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| author | Dalal Hamid Mohammed Younis |
| author_facet | Dalal Hamid Mohammed Younis |
| author_sort | Dalal Hamid |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d73b4bfaf27541168806c0d61efbf656 |
| institution | OA Journals |
| issn | 1815-4816 2311-7990 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Mosul University |
| record_format | Article |
| series | Al-Rafidain Journal of Computer Sciences and Mathematics |
| spelling | doaj-art-d73b4bfaf27541168806c0d61efbf6562025-08-20T02:19:31ZengMosul UniversityAl-Rafidain Journal of Computer Sciences and Mathematics1815-48162311-79902024-12-01182526410.33899/csmj.2024.148886.1115185890Electronic Health Data Records for Diabetes Patients Based on Deep Learning Models: A ReviewDalal Hamid0Mohammed Younis1University of Mosul / College of Computer Sciences and Mathematics / Department of Computer SciencesUniversity of Mosul - College of Computer Sciences and Mathematics – Department of Computer SciencesThe 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.https://csmj.uomosul.edu.iq/article_185890_2df03c57a8b26bd238a5e3759e9c1a91.pdfdeep learningdiabeteselectronic health recordsrecurrent neural networksconvolutional neural networks |
| spellingShingle | Dalal Hamid Mohammed Younis Electronic Health Data Records for Diabetes Patients Based on Deep Learning Models: A Review Al-Rafidain Journal of Computer Sciences and Mathematics deep learning diabetes electronic health records recurrent neural networks convolutional neural networks |
| title | Electronic Health Data Records for Diabetes Patients Based on Deep Learning Models: A Review |
| title_full | Electronic Health Data Records for Diabetes Patients Based on Deep Learning Models: A Review |
| title_fullStr | Electronic Health Data Records for Diabetes Patients Based on Deep Learning Models: A Review |
| title_full_unstemmed | Electronic Health Data Records for Diabetes Patients Based on Deep Learning Models: A Review |
| title_short | Electronic Health Data Records for Diabetes Patients Based on Deep Learning Models: A Review |
| title_sort | electronic health data records for diabetes patients based on deep learning models a review |
| topic | deep learning diabetes electronic health records recurrent neural networks convolutional neural networks |
| url | https://csmj.uomosul.edu.iq/article_185890_2df03c57a8b26bd238a5e3759e9c1a91.pdf |
| work_keys_str_mv | AT dalalhamid electronichealthdatarecordsfordiabetespatientsbasedondeeplearningmodelsareview AT mohammedyounis electronichealthdatarecordsfordiabetespatientsbasedondeeplearningmodelsareview |