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Revolutionizing Healthcare: Exploring the Latest Advances in Digital Health Technology
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Revolutionizing Healthcare: Exploring the Latest Advances in Digital Health Technology

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 23013

Special Issue Editors

Dr. Hossein Hassani

E-Mail Website
Guest Editor
1. International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
2. Research Institute of Energy Management and Planning (RIEMP), University of Tehran, Tehran 1417466191, Iran
Interests: big data; AI; machine learning; statistics; digital twins; digital health; advanced technology
Special Issues, Collections and Topics in MDPI journals
Dr. Steve MacFeely

E-Mail Website
Guest Editor
Data and Analytics Division, World Health Organization, 1201 Geneva, Switzerland
Interests: statistics; digital healthcare; advanced technology

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for our upcoming special issue on "Revolutionizing Healthcare: Exploring the Latest Advances in Digital Health Technology".

Digital health technology has been rapidly advancing, and it has the potential to revolutionize healthcare delivery, improve patient outcomes, and reduce costs. This special issue aims to provide a platform for researchers, academics, and practitioners to share their latest research, best practices, and insights on the use of digital health technology in healthcare.

We welcome original research articles, review papers, case studies, and perspectives on the following topics (but not limited to):

  • Telemedicine and telehealth
  • Artificial intelligence and machine learning in healthcare
  • Big data analytics in healthcare
  • Internet of Things (IoT) in healthcare
  • Wearable technology and mobile health applications
  • Blockchain technology in healthcare
  • Virtual and augmented reality in healthcare
  • Electronic health records and health information systems
  • Patient engagement and digital health interventions

We encourage submissions that demonstrate interdisciplinary and collaborative research, and that address the challenges, opportunities, and ethical considerations associated with the use of digital health technology in healthcare.

Dr. Hossein Hassani
Dr. Steve MacFeely
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital health technology
  • telemedicine, artificial intelligence
  • big data analytics
  • Internet of Things
  • wearable technology
  • blockchain
  • patient engagement
  • interdisciplinary research

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

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31 pages, 4560 KiB  
Article
Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model
by A. M. Mutawa
Big Data Cogn. Comput. 2025, 9(1), 13; https://doi.org/10.3390/bdcc9010013 - 14 Jan 2025
Viewed by 571
Abstract
Intensive Care Units (ICUs) have been in great demand worldwide since the COVID-19 pandemic, necessitating organized allocation. The spike in critical care patients has overloaded ICUs, which along with prolonged hospitalizations, has increased workload for medical personnel and lead to a significant shortage [...] Read more.
Intensive Care Units (ICUs) have been in great demand worldwide since the COVID-19 pandemic, necessitating organized allocation. The spike in critical care patients has overloaded ICUs, which along with prolonged hospitalizations, has increased workload for medical personnel and lead to a significant shortage of resources. The study aimed to improve resource management by quickly and accurately identifying patients who need ICU admission. We designed an intelligent decision support system that employs machine learning (ML) to anticipate COVID-19 ICU admissions in Kuwait. Our algorithm examines several clinical and demographic characteristics to identify high-risk individuals early in illness diagnosis. We used 4399 patients to identify ICU admission with predictors such as shortness of breath, high D-dimer values, and abnormal chest X-rays. Any data imbalance was addressed by employing cross-validation along with the Synthetic Minority Oversampling Technique (SMOTE), the feature selection was refined using backward elimination, and the model interpretability was improved using Shapley Additive Explanations (SHAP). We employed various ML classifiers, including support vector machines (SVM). The SVM model surpasses all other models in terms of precision (0.99) and area under curve (AUC, 0.91). This study investigated the healthcare process during a pandemic, facilitating ML-based decision-making solutions to confront healthcare problems. Full article
(This article belongs to the Special Issue Revolutionizing Healthcare: Exploring the Latest Advances in Digital Health Technology)
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32 pages, 11502 KiB  
Article
DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography
by Ismael Santarrosa-López, Giner Alor-Hernández, Maritza Bustos-López, Jonathan Hernández-Capistrán, Laura Nely Sánchez-Morales, José Luis Sánchez-Cervantes and Humberto Marín-Vega
Big Data Cogn. Comput. 2025, 9(1), 3; https://doi.org/10.3390/bdcc9010003 - 30 Dec 2024
Viewed by 695
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is often challenging due to subjective assessments and symptom variability, which can delay accurate detection and treatment. To address these limitations, this study introduces DETEC-ADHD, a web-based application that combines machine learning (ML) techniques with multi-source data [...] Read more.
Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is often challenging due to subjective assessments and symptom variability, which can delay accurate detection and treatment. To address these limitations, this study introduces DETEC-ADHD, a web-based application that combines machine learning (ML) techniques with multi-source data to enhance diagnostic accuracy. Unlike traditional approaches, DETEC-ADHD primarily utilizes extensive personal, medical, and psychological information for its initial classification. DETEC-ADHD further refines diagnoses by identifying ADHD subtypes (inattentive, hyperactive, combined) through theta/beta wave ratio analysis from EEG data, offering neurophysiological insights that complement its classification process. Logistic Regression, selected for its validated accuracy and reliability, served as the ML model for the app. The case studies demonstrated DETEC-ADHD’s effectiveness, achieving 100% accuracy in children and 90% in adults. By integrating diverse data sources with real-time EEG analysis, DETEC-ADHD provides a scalable, cost-effective, and accessible solution for ADHD detection and subtype identification, addressing diagnostic challenges and supporting healthcare providers, particularly in resource-limited environments. Full article
(This article belongs to the Special Issue Revolutionizing Healthcare: Exploring the Latest Advances in Digital Health Technology)
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33 pages, 918 KiB  
Article
The Relative Importance of Key Factors for Integrating Enterprise Resource Planning (ERP) Systems and Performance Management Practices in the UAE Healthcare Sector
by Karam Al-Assaf, Wadhah Alzahmi, Ryan Alshaikh, Zied Bahroun and Vian Ahmed
Big Data Cogn. Comput. 2024, 8(9), 122; https://doi.org/10.3390/bdcc8090122 - 13 Sep 2024
Cited by 1 | Viewed by 4099
Abstract
This study examines integrating Enterprise Resource Planning (ERP) systems with performance management (PM) practices in the UAE healthcare sector, identifying key factors for successful adoption. It addresses a critical gap by analyzing the interplay between ERP systems and PM to enhance operational efficiency, [...] Read more.
This study examines integrating Enterprise Resource Planning (ERP) systems with performance management (PM) practices in the UAE healthcare sector, identifying key factors for successful adoption. It addresses a critical gap by analyzing the interplay between ERP systems and PM to enhance operational efficiency, patient care, and administrative processes. A literature review identified thirty-six critical factors, refined through expert interviews to highlight nine weak integration areas and two new factors. An online survey with 81 experts, who rated the 38 factors on a five-point Likert scale, provided data to calculate the Relative Importance Index (RII). The results reveal that employee involvement in performance metrics and effective organizational measures significantly impact system effectiveness and alignment. Mid-tier factors such as leadership and managerial support are essential for integration momentum, while foundational elements like infrastructure, scalability, security, and compliance are crucial for long-term success. The study recommends a holistic approach to these factors to maximize ERP benefits, offering insights for healthcare administrators and policymakers. Additionally, it highlights the need to address the challenges, opportunities, and ethical considerations associated with using digital health technology in healthcare. Future research should explore ERP integration challenges in public and private healthcare settings, tailoring systems to specific organizational needs. Full article
(This article belongs to the Special Issue Revolutionizing Healthcare: Exploring the Latest Advances in Digital Health Technology)
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28 pages, 1665 KiB  
Article
Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students
by Dhiaa Musleh, Ali Alkhwaja, Ibrahim Alkhwaja, Mohammed Alghamdi, Hussam Abahussain, Mohammed Albugami, Faisal Alfawaz, Said El-Ashker and Mohammed Al-Hariri
Big Data Cogn. Comput. 2024, 8(3), 31; https://doi.org/10.3390/bdcc8030031 - 13 Mar 2024
Cited by 2 | Viewed by 3476
Abstract
Obesity is increasingly becoming a prevalent health concern among adolescents, leading to significant risks like cardiometabolic diseases (CMDs). The early discovery and diagnosis of CMD is essential for better outcomes. This study aims to build a reliable artificial intelligence model that can predict [...] Read more.
Obesity is increasingly becoming a prevalent health concern among adolescents, leading to significant risks like cardiometabolic diseases (CMDs). The early discovery and diagnosis of CMD is essential for better outcomes. This study aims to build a reliable artificial intelligence model that can predict CMD using various machine learning techniques. Support vector machines (SVMs), K-Nearest neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Gradient Boosting are five robust classifiers that are compared in this study. A novel “risk level” feature, derived through fuzzy logic applied to the Conicity Index, as a novel feature, which was previously unused, is introduced to enhance the interpretability and discriminatory properties of the proposed models. As the Conicity Index scores indicate CMD risk, two separate models are developed to address each gender individually. The performance of the proposed models is assessed using two datasets obtained from 295 records of undergraduate students in Saudi Arabia. The dataset comprises 121 male and 174 female students with diverse risk levels. Notably, Logistic Regression emerges as the top performer among males, achieving an accuracy score of 91%, while Gradient Boosting lags with a score of 72%. Among females, both Support Vector Machine and Logistic Regression lead with an accuracy score of 87%, while Random Forest performs least optimally with a score of 80%. Full article
(This article belongs to the Special Issue Revolutionizing Healthcare: Exploring the Latest Advances in Digital Health Technology)
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26 pages, 1185 KiB  
Article
Driving Excellence in Official Statistics: Unleashing the Potential of Comprehensive Digital Data Governance
by Hossein Hassani and Steve MacFeely
Big Data Cogn. Comput. 2023, 7(3), 134; https://doi.org/10.3390/bdcc7030134 - 29 Jul 2023
Cited by 4 | Viewed by 5434
Abstract
With the ubiquitous use of digital technologies and the consequent data deluge, official statistics faces new challenges and opportunities. In this context, strengthening official statistics through effective data governance will be crucial to ensure reliability, quality, and access to data. This paper presents [...] Read more.
With the ubiquitous use of digital technologies and the consequent data deluge, official statistics faces new challenges and opportunities. In this context, strengthening official statistics through effective data governance will be crucial to ensure reliability, quality, and access to data. This paper presents a comprehensive framework for digital data governance for official statistics, addressing key components, such as data collection and management, processing and analysis, data sharing and dissemination, as well as privacy and ethical considerations. The framework integrates principles of data governance into digital statistical processes, enabling statistical organizations to navigate the complexities of the digital environment. Drawing on case studies and best practices, the paper highlights successful implementations of digital data governance in official statistics. The paper concludes by discussing future trends and directions, including emerging technologies and opportunities for advancing digital data governance. Full article
(This article belongs to the Special Issue Revolutionizing Healthcare: Exploring the Latest Advances in Digital Health Technology)
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Review

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30 pages, 3527 KiB  
Review
Advancing Dental Diagnostics: A Review of Artificial Intelligence Applications and Challenges in Dentistry
by Dhiaa Musleh, Haya Almossaeed, Fay Balhareth, Ghadah Alqahtani, Norah Alobaidan, Jana Altalag and May Issa Aldossary
Big Data Cogn. Comput. 2024, 8(6), 66; https://doi.org/10.3390/bdcc8060066 - 7 Jun 2024
Cited by 3 | Viewed by 7252
Abstract
The rise of artificial intelligence has created and facilitated numerous everyday tasks in a variety of industries, including dentistry. Dentists have utilized X-rays for diagnosing patients’ ailments for many years. However, the procedure is typically performed manually, which can be challenging and time-consuming [...] Read more.
The rise of artificial intelligence has created and facilitated numerous everyday tasks in a variety of industries, including dentistry. Dentists have utilized X-rays for diagnosing patients’ ailments for many years. However, the procedure is typically performed manually, which can be challenging and time-consuming for non-specialized specialists and carries a significant risk of error. As a result, researchers have turned to machine and deep learning modeling approaches to precisely identify dental disorders using X-ray pictures. This review is motivated by the need to address these challenges and to explore the potential of AI to enhance diagnostic accuracy, efficiency, and reliability in dental practice. Although artificial intelligence is frequently employed in dentistry, the approaches’ outcomes are still influenced by aspects such as dataset availability and quantity, chapter balance, and data interpretation capability. Consequently, it is critical to work with the research community to address these issues in order to identify the most effective approaches for use in ongoing investigations. This article, which is based on a literature review, provides a concise summary of the diagnosis process using X-ray imaging systems, offers a thorough understanding of the difficulties that dental researchers face, and presents an amalgamative evaluation of the performances and methodologies assessed using publicly available benchmarks. Full article
(This article belongs to the Special Issue Revolutionizing Healthcare: Exploring the Latest Advances in Digital Health Technology)
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