The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2025.0160136
PDF

Convolutional Neural Network and Bidirectional Long Short-Term Memory for Personalized Treatment Analysis Using Electronic Health Records

Author 1: Prasanthi Yavanamandha
Author 2: D. S. Rao

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 1, 2025.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Correct precision techniques have far not been introduced for modeling the modality risk in Intensive Care Unit (ICU) patients. Traditional mortality risk prediction techniques effectively extract the data in longitudinal Electronic Health Records (EHRs), that ignore the difficult relationship and interactions among variables and time dependency in longitudinal records. The proposed work, developed the Convolutional Neural Network – Bidirectional-Long Short-Term Memory (CNN-Bi-LSTM) method for personalized treatment analysis using EHR data. The CNN extracts the significant features from relevant features, focused on spatial-based relationships. Then, the Bi-LSTM layer captured the sequential dependencies and temporal relationships in patient histories that are essential to understand the treatment results. The Circle Levy flight – Ladybug Beetle Optimization (CL-LBO) integrates the circle chaotic map and Levy flight process in traditional LBO to select relevant features for classification. The proposed method reached 99.85% accuracy, 99.60% precision, 99.50% recall, 99.55% f1-score, and 99.95% Area Under Curve (AUC) when compared to LSTM.

Keywords: Bidirectional-long short-term memory; circle chaotic map; convolutional neural network; electronic medical records; Intensive Care Unit (ICU); ladybug beetle optimization

Prasanthi Yavanamandha and D. S. Rao, “Convolutional Neural Network and Bidirectional Long Short-Term Memory for Personalized Treatment Analysis Using Electronic Health Records” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160136

@article{Yavanamandha2025,
title = {Convolutional Neural Network and Bidirectional Long Short-Term Memory for Personalized Treatment Analysis Using Electronic Health Records},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160136},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160136},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Prasanthi Yavanamandha and D. S. Rao}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org