Biomedical Informatics and Smart Healthcare

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ISSN: 3068-5524
Biomedical Informatics and Smart Healthcare focuses on the integration of advanced informatics techniques with healthcare technologies to enhance patient care, improve clinical decision-making, and advance medical research.
DOI Prefix: 10.62762/BISH

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Recent Articles

Open Access | Research Article | 24 June 2025
Exploring the Temporal Dynamics and Causal Interactions Between the Amygdala and vmPFC: A Multidimensional Approach Using Time-Lagged Correlation, Granger Causality, and Entropy Analysis
Biomedical Informatics and Smart Healthcare | Volume 1, Issue 1: 27-34, 2025 | DOI: 10.62762/BISH.2025.346171
Abstract
Understanding the dynamic interaction between the amygdala and the ventromedial prefrontal cortex (vmPFC) is essential for unraveling the neural mechanisms underlying emotion regulation. This study introduces a multidimensional analytical framework that integrates functional connectivity, time-lagged correlation, Granger causality analysis (GCA), and entropy-based complexity metrics to explore the amygdala–vmPFC relationship during emotionally aversive tasks using intracranial EEG (iEEG) data. Our findings reveal a significant negative functional correlation (r = -0.009, p < 0.05) between the amygdala and vmPFC, indicating an inhibitory relationship. Time-lagged correlation analysis furthe... More >

Graphical Abstract
Exploring the Temporal Dynamics and Causal Interactions Between the Amygdala and vmPFC: A Multidimensional Approach Using Time-Lagged Correlation, Granger Causality, and Entropy Analysis
Open Access | Research Article | 03 June 2025 | Cited: Crossref logo  6 , Scopus 7
Diabetic Retinopathy Detection and Analysis with Convolutional Neural Networks and Vision Transformer
Biomedical Informatics and Smart Healthcare | Volume 1, Issue 1: 18-26, 2025 | DOI: 10.62762/BISH.2025.724307
Abstract
Diabetic Retinopathy occurs when elevated blood sugar levels damage retinal blood vessels, potentially leading to vision impairment. In this paper, we have tested the performance of CNN, ViT and their hybrid models. The dataset used is publicly available on Kaggle and the dataset contained around 35,000 retinal images which were divided into 5 classes namely No DR, Mild DR, Moderate DR, Severe DR and Proliferative DR. In CNN we tested 4 different architectures in which we achieved the best accuracy of 75.4% with Resnet50 architecture and with ViT model we achieved an accuracy of 83.9% and from the hybrid model we achieved an accuracy of 88.4% from the Resnet50 + ViT. The results shown by the... More >

Graphical Abstract
Diabetic Retinopathy Detection and Analysis with Convolutional Neural Networks and Vision Transformer
Open Access | Research Article | 02 June 2025 | Cited: Crossref logo  1 , Scopus 1
Optimizing ICU Resource Allocation During the COVID-19 Crisis: An AI-Driven Approach
Biomedical Informatics and Smart Healthcare | Volume 1, Issue 1: 9-17, 2025 | DOI: 10.62762/BISH.2025.457428
Abstract
The COVID-19 pandemic exerted immense pressure on healthcare systems globally, including in Morocco, where the demand for intensive care unit (ICU) beds frequently surpassed available capacity—at times doubling it. This crisis underscored the critical need for accurate prediction of ICU length of stay (LOS) to optimize resource allocation, enhance patient care, and reduce healthcare costs. This study aims to leverage artificial intelligence (AI) to predict and optimize ICU resource allocation during the COVID-19 crisis, ensuring efficient patient triage and resource management. By integrating Random Forest (RF) and Deep Neural Networks (DNN), the research demonstrates improved accuracy in... More >

Graphical Abstract
Optimizing ICU Resource Allocation During the COVID-19 Crisis: An AI-Driven Approach
Open Access | Research Article | 30 May 2025
HDSF: A Healthcare Decision Support Framework to Provide A Seamless and Adaptable Patient Experience
Biomedical Informatics and Smart Healthcare | Volume 1, Issue 1: 1-8, 2025 | DOI: 10.62762/BISH.2025.352565
Abstract
Healthcare decision support framework(HDSF), a comprehensive web application framework designed to revolutionize healthcare accessibility and efficiency. HDSF integrates various facilities including online appointment booking, virtual doctor consultations, symptom detection, detailed prescription management, home nursing appointment scheduling, and updates on local health camps with Google Maps integration for navigation. The application employs a robust architecture with a front end developed using HTML, CSS, and Bootstrap, while the back-end leverages Java and Java Servlet technologies. Data management is facilitated by MySQL, and the application is developed within the Eclipse IDE and XAM... More >

Graphical Abstract
HDSF: A Healthcare Decision Support Framework to Provide A Seamless and Adaptable Patient Experience

Journal Statistics

75
Authors
11
Countries / Regions
28
Articles
28
Scopus Citations
35.7% Cited
2025
Published Since
70,463
Article Views
17,984
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Biomedical Informatics and Smart Healthcare
Biomedical Informatics and Smart Healthcare
eISSN: 3068-5524
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