cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota pekanbaru,
Riau
INDONESIA
IT JOURNAL RESEARCH AND DEVELOPMENT
Published by Universitas Islam Riau
ISSN : 25284061     EISSN : 25284053     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
Information Technology Journal Research and Development (ITJRD) adalah Jurnal Ilmiah yang dibangun oleh Prodi Teknik Informatika, Universitas Islam Riau untuk memberikan sarana bagi para akademisi dan peneliti untuk mempublikasikan tulisan dan karya ilmiah di Bidang Teknologi Informatika. Adapun ruang lingkup dalam jurnal ini meliputi bidang penelitian di teknik informatika, ilmu komputer, jaringan komputer, sistem informasi, desain grafis, pengelolaan citra dan multimedia.
Arjuna Subject : -
Articles 211 Documents
 18   19   20   21   22 
Performance Metrics of Deep Learning Models in EHR Analysis: A Systematic Review Adoghe, Olumhense; Ikharo , Braimoh; Amhenrior, Henry
IT Journal Research and Development Vol. 10 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.24176

Abstract

The rapid growth of Electronic Health Records (EHRs), generating 30% of global data with an annual increase of 36%, presents both opportunities and challenges for healthcare analytics. Traditional methods struggle with the complexity of EHR data, prompting the adoption of deep learning (DL) to uncover patterns and improve patient care. This systematic review, adhering to PRISMA guidelines, analyzes 82 peer-reviewed studies from 2017 to 2024 to evaluate the performance of five DL architectures Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, hybrid CNN-LSTM models, and Transformer-based models in EHR applications. CNNs achieved high accuracy (90–95%) in medical imaging, while LSTMs excelled in sequential tasks like ICU readmission prediction (88–93%). Hybrid CNN-LSTM models outperformed others, reaching up to 96% accuracy in multimodal tasks such as sepsis prediction [79]. Transformers, though computationally intensive, showed strong potential for clinical note analysis. Despite these advances, challenges like model interpretability, data privacy, and computational demands persist. This study provides a comprehensive benchmark of DL performance metrics across EHR tasks, offering insights into their practical applications and highlighting future directions, such as explainable AI and federated learning, to enable scalable, privacy-preserving healthcare solutions. By synthesizing these findings, this review guides researchers and clinicians in leveraging DL to enhance data-driven healthcare.

Page 22 of 22 | Total Record : 211

 18   19   20   21   22 

Filter by Year

2016 2025


Reset
Filter By Issues
All Issue Vol. 10 No. 2 (2025) Vol. 9 No. 2 (2025) Vol. 9 No. 1 (2024) Vol. 8 No. 2 (2024) Vol. 8 No. 1 (2023) Vol. 7 No. 2 (2023) Vol. 7 No. 1 (2022) Vol. 6 No. 2 (2022) Vol. 6 No. 1 (2021) Vol. 5 No. 2 (2021) Vol. 5 No. 1 (2020) Vol. 4 No. 2 (2020) Vol. 4 No. 1 (2019) Vol. 3 No. 2 (2019) Vol 3 No 2 (2019) Vol 3 No 1 (2018) Vol. 3 No. 1 (2018) Vol 2 No 2 (2018) Vol. 2 No. 2 (2018) Vol 2 No 1 (2017) Vol. 2 No. 1 (2017) Vol 1 No 2 (2017) Vol. 1 No. 2 (2017) Vol. 1 No. 1 (2016) Vol 1 No 1 (2016) Article In Press More Issue