Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
4.3
Journals
Information
Special Issues
Advancements in Healthcare Data Science: Innovations, Challenges and Applications
share announcement

Advancements in Healthcare Data Science: Innovations, Challenges and Applications

A Special Issue of Information (ISSN 2078-2489) belonging to the section "Biomedical Information and Health".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 17530

Editor

Dr. Muneer Ahmad

E-Mail Website
Guest Editor
Department of Computer Science, University of Roehampton, Roehampton Lane SW15 5 PH, UK
Interests: artificial intelligence; smart healthcare; disease diagnosis; drug discovery; clinical decision support systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the transformative impact of data science on healthcare, focusing on the latest innovations, challenges, and applications in the field. With the rapid evolution of healthcare technologies and the proliferation of healthcare data, data science has emerged as a powerful tool for revolutionizing healthcare delivery, improving patient outcomes, and enhancing clinical decision making. This Special Issue seeks to bring together cutting-edge research and practical insights from experts in academia, industry, and healthcare institutions to address key challenges, explore novel methodologies, and showcase successful applications of data science in healthcare settings.

Non-Exhaustive List of Contents for the Special Issue:

  • Predictive Analytics for Disease Diagnosis: Focuses on the development and validation of predictive models using healthcare data for early disease detection, prognosis, and risk stratification.
  • Personalized Medicine and Precision Healthcare: Explores personalized medicine approaches that leverage patient data, genomic information, and machine learning techniques to tailor treatments and interventions for individual patients.
  • Drug Discovery and Development: Highlights innovative data science approaches for accelerating drug discovery, optimizing clinical trials, and repurposing existing drugs using computational methods and big data analytics.
  • Clinical Decision Support Systems: Discusses the design, implementation, and evaluation of clinical decision support systems powered by artificial intelligence, natural language processing, and predictive analytics to assist healthcare providers in making evidence-based decisions.
  • Healthcare Data Privacy and Security: Addresses the critical issues surrounding healthcare data privacy, security, and ethics, including data anonymization techniques, secure data sharing frameworks, and regulatory compliance in healthcare analytics.
  • Telemedicine and Remote Monitoring: Examines the role of data science in enabling telemedicine platforms, remote patient monitoring systems, and virtual care delivery models for improving access to healthcare services and managing chronic conditions.
  • Healthcare Data Visualization and Interpretation: Focuses on innovative data visualization techniques and interactive tools for interpreting complex healthcare data, communicating insights to stakeholders, and facilitating data-driven decision making in healthcare organizations.
  • Case Studies and Applications: Features real-world case studies, success stories, and practical applications of data science in healthcare, showcasing the impact of data-driven approaches on patient care, population health management, and healthcare operations.
  • Future Directions and Challenges: Discusses emerging trends, future directions, and unresolved challenges in healthcare data science, including opportunities for interdisciplinary collaboration, ethical considerations, and the adoption of innovative technologies in healthcare delivery.

Dr. Muneer Ahmad
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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

  • predictive analytics
  • personalized medicine
  • clinical decision support systems
  • drug discovery
  • telemedicine
  • remote monitoring
  • healthcare data privacy
  • data visualization
  • machine learning
  • artificial intelligence
  • precision healthcare
  • healthcare data security
  • electronic health records
  • population health management
  • healthcare analytics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (8 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

36 pages, 3920 KB  
Article
Drug–Drug Interaction Prediction Using SMOTE and Gray Wolf Optimizer: Comparative Analysis of Machine Learning and Deep Learning Models
by Basma Elsharkawy, Amira Abdelatey, O. G. El Barbary, Hatem Abdelkader and Nesma Mahmoud
Information 2026, 17(5), 467; https://doi.org/10.3390/info17050467 - 12 May 2026
Cited by 1 | Viewed by 976
Abstract
Drug–drug interaction (DDI) prediction plays a critical role in optimizing therapeutic outcomes and enhancing patient safety. DDIs pose challenges in drug discovery, often leading to adverse effects, reduced efficacy, or unexpected outcomes. AI in DDIs acts as an effective tool for analyzing and [...] Read more.
Drug–drug interaction (DDI) prediction plays a critical role in optimizing therapeutic outcomes and enhancing patient safety. DDIs pose challenges in drug discovery, often leading to adverse effects, reduced efficacy, or unexpected outcomes. AI in DDIs acts as an effective tool for analyzing and predicting DDIs which introduced efficient computational approaches to DDI prediction. This paper aims to provide a comprehensive understanding of how ML and DL models perform in DDI prediction. This paper presents a comparative analysis based on key performance metrics such as accuracy, precision, recall and F-score for different ML and DL Models. We used Synthetic Minority Oversampling Technique (SMOTE) and the Gray Wolf Optimizer (GWO) which achieved the best accuracy of 95.42%. Combining the GWO with SMOTE addresses both optimization and data imbalance challenges in DDI prediction. Effectively, SMOTE addresses the class imbalance issue that leads to poor performance. SMOTE improves model performance by generating synthetic examples of the minority class rather than merely duplicating existing ones. This helps create a balanced dataset, enabling the model to learn the decision boundaries more accurately. SMOTE reduces the risk of overfitting. The GWO serves as a metaheuristic optimization framework that enhances model performance by guiding optimal feature selection subsets. This optimization process improves the model’s ability to capture complex, non-linear interaction patterns, leading to enhanced results. In our result, we achieve an accuracy of over 94% which helps in drug safety and therapeutic decision-making in health informatics. Full article
(This article belongs to the Special Issue Advancements in Healthcare Data Science: Innovations, Challenges and Applications)
►▼ Show Figures

Figure 1

26 pages, 5754 KB  
Article
From Data to Diagnosis: A Machine Learning-Enabled Framework for Early Sepsis Prediction and Prevention
by Hassan Harb
Information 2026, 17(5), 430; https://doi.org/10.3390/info17050430 - 30 Apr 2026
Cited by 1 | Viewed by 1512
Abstract
The rising prevalence of chronic diseases, driven by population ageing, emerging pathogens, and evolving lifestyles, necessitates stronger healthcare systems that integrate effective prevention with timely intervention. Sepsis remains one of the most critical and life-threatening conditions, associated with high incidence, mortality, and morbidity, [...] Read more.
The rising prevalence of chronic diseases, driven by population ageing, emerging pathogens, and evolving lifestyles, necessitates stronger healthcare systems that integrate effective prevention with timely intervention. Sepsis remains one of the most critical and life-threatening conditions, associated with high incidence, mortality, and morbidity, and frequently progressing to multiple organ dysfunction and septic shock. Early identification is therefore essential to improve patient outcomes. In this work, we propose a rapid and accurate data-driven framework for early sepsis prediction. The framework comprises four stages: data collection, preprocessing, preparation, and classification. Real-world clinical data from 1000 patients are utilized for early risk assessment. Data preprocessing focuses on cleaning and extracting clinically relevant features, followed by data preparation steps including labeling, dataset splitting, class balancing, and feature scaling. Multiple machine learning and neural network models are then implemented, with optimized parameter selection to enhance predictive performance. Finally, a deployment module enables healthcare professionals to leverage the trained models for real-time patient status assessment, supporting timely clinical decision-making. Extensive experimental results demonstrate that the proposed framework achieves fast and accurate discrimination between septic and non-septic patients, outperforming existing state-of-the-art approaches. Full article
(This article belongs to the Special Issue Advancements in Healthcare Data Science: Innovations, Challenges and Applications)
►▼ Show Figures

Graphical abstract

28 pages, 1320 KB  
Article
WCGAN-GA-RF: Healthcare Fraud Detection via Generative Adversarial Networks and Evolutionary Feature Selection
by Junze Cai, Shuhui Wu, Yawen Zhang, Jiale Shao and Yuanhong Tao
Information 2026, 17(4), 315; https://doi.org/10.3390/info17040315 - 24 Mar 2026
Viewed by 683
Abstract
Healthcare fraud poses significant risks to insurance systems, undermining both financial sustainability and equitable access to care. Accurate detection of fraudulent claims is therefore critical to ensuring the integrity of healthcare insurance operations. However, the increasing sophistication of fraud techniques and limited data [...] Read more.
Healthcare fraud poses significant risks to insurance systems, undermining both financial sustainability and equitable access to care. Accurate detection of fraudulent claims is therefore critical to ensuring the integrity of healthcare insurance operations. However, the increasing sophistication of fraud techniques and limited data availability have undermined the performance of traditional detection approaches. To address these challenges, this paper proposes WCGAN-GA-RF, an integrated fraud detection framework that synergistically combines Wasserstein Conditional Generative Adversarial Network with gradient penalty (WCGAN-GP) for synthetic data generation, genetic algorithm-based feature selection (GA-RF) for dimensionality reduction, and Random Forest (RF) for classification. The proposed framework was empirically validated on a real-world dataset of 16,000 healthcare insurance claims from a Chinese healthcare technology firm, characterized by a 16:1 class imbalance ratio (5.9% fraudulent samples) and 118 original features. Using a stratified 80/20 train–test split with results averaged over five independent runs, the WCGAN-GA-RF framework achieved a precision of 96.47±0.5%, a recall of 97.05±0.4%, and an F1-score of 96.26±0.4%. Notably, the GA-RF component achieved a 65% feature reduction (from 80 to 28 features) while maintaining competitive detection accuracy. Comparative experiments demonstrate that the proposed approach outperforms conventional oversampling methods, including Random Oversampling (ROS), Synthetic Minority Oversampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN), particularly in handling high-dimensional, severely imbalanced healthcare fraud data. Full article
(This article belongs to the Special Issue Advancements in Healthcare Data Science: Innovations, Challenges and Applications)
►▼ Show Figures

Figure 1

18 pages, 6105 KB  
Article
Improving Skin Lesion Detection with Transformer-Based Architectures
by Andrés Villamarín-Olmos and Diego Renza
Information 2026, 17(2), 130; https://doi.org/10.3390/info17020130 - 1 Feb 2026
Viewed by 872
Abstract
This article describes the methodology for adjusting and comparing eleven variants of Transformer architectures for the classification of skin lesions using images: five variants of Google’s Vision Transformer (ViT) and six variants of Microsoft’s Swin Transformer. We present the methodology used to achieve [...] Read more.
This article describes the methodology for adjusting and comparing eleven variants of Transformer architectures for the classification of skin lesions using images: five variants of Google’s Vision Transformer (ViT) and six variants of Microsoft’s Swin Transformer. We present the methodology used to achieve these results, which includes meticulous hyperparameter tuning and a robust data augmentation strategy to address the class imbalance problem. This approach allowed us to surpass the state of the art on the DermaMNIST dataset with respect to CNN-based models, and achieve very competitive results on the ISIC Challenge 2019 dataset with respect to Transformer-based models. In addition, we employed the CheferCAM method to provide visual explanations that identify the most influential image regions in the models’ predictions. Full article
(This article belongs to the Special Issue Advancements in Healthcare Data Science: Innovations, Challenges and Applications)
►▼ Show Figures

Graphical abstract

29 pages, 895 KB  
Article
The Feasibility and Acceptability of AI-Based eGuide for Healthcare Centers in Oman
by Yasir Abdelgadir Mohamed, Mohamed Bashir, Akbar Khanan and Dil Nawaz Hakro
Information 2025, 16(12), 1093; https://doi.org/10.3390/info16121093 - 10 Dec 2025
Cited by 2 | Viewed by 1774
Abstract
The rapid advancement of artificial intelligence (AI) in healthcare delivery has introduced innovative tools to improve patient care, streamline administrative processes, and bridge accessibility gaps. This study assesses how end-users perceive the practicality and usability of a proposed AI-enabled eGuide within Omani healthcare [...] Read more.
The rapid advancement of artificial intelligence (AI) in healthcare delivery has introduced innovative tools to improve patient care, streamline administrative processes, and bridge accessibility gaps. This study assesses how end-users perceive the practicality and usability of a proposed AI-enabled eGuide within Omani healthcare facilities, addressing cultural, linguistic, and regulatory requirements unique to the Sultanate. Through a mixed-methods framework combining stakeholder analysis, technological readiness assessment, and socio-cultural adaptation strategies, the research identifies the operational, economic, and ethical viability of the system. The current research results suggest that regulatory alignment, stakeholder engagement, and proper localization of AI-based eGuides will significantly enhance patient navigation after being tested on a wider dataset or real-world healthcare environments, reduce healthcare delivery bottlenecks, and increase patient satisfaction. Furthermore, digital literacy disparities, data privacy compliance, and infrastructure variability challenges need to be planned strategically and handled with care. This study offers a roadmap for policymakers and healthcare administrators to adopt AI-enabled eGuide systems that are both technically feasible and socially acceptable within the Omani healthcare ecosystem. Full article
(This article belongs to the Special Issue Advancements in Healthcare Data Science: Innovations, Challenges and Applications)
►▼ Show Figures

Graphical abstract

28 pages, 4137 KB  
Article
Epidemic Modeling in Satellite Towns and Interconnected Cities: Data-Driven Simulation and Real-World Lockdown Validation
by Rafaella S. Ferreira, Wallace Casaca, João F. C. A. Meyer, Marilaine Colnago, Mauricio A. Dias and Rogério G. Negri
Information 2025, 16(4), 299; https://doi.org/10.3390/info16040299 - 8 Apr 2025
Cited by 1 | Viewed by 1149
Abstract
Understanding the effectiveness of different quarantine strategies is crucial for controlling the spread of COVID-19, particularly in regions with limited data. This study presents a SCIRD-inspired model to simulate the transmission dynamics of COVID-19 in medium-sized cities and their surrounding satellite towns. Unlike [...] Read more.
Understanding the effectiveness of different quarantine strategies is crucial for controlling the spread of COVID-19, particularly in regions with limited data. This study presents a SCIRD-inspired model to simulate the transmission dynamics of COVID-19 in medium-sized cities and their surrounding satellite towns. Unlike previous works that focus primarily on large urban centers or homogeneous populations, our approach incorporates intercity mobility and evaluates the impact of spatially differentiated interventions. By analyzing lockdown strategies implemented during the first year of the pandemic, we demonstrate that short, localized lockdowns are highly effective in reducing virus propagation, while intermittent restrictions balance public health concerns with socioeconomic demands. A key contribution of this study is the validation of the epidemic model using real-world data from the 2021 lockdown that occurred in a medium-sized city, confirming its predictive accuracy and adaptability to different contexts. Additionally, we provide a detailed analysis of how mobility patterns between municipalities influence infection spread, offering a more comprehensive mathematical framework for decision-making. These findings advance the understanding of epidemic control in regions with sparse data and provide evidence-based insights to inform public health policies in similar contexts. Full article
(This article belongs to the Special Issue Advancements in Healthcare Data Science: Innovations, Challenges and Applications)
►▼ Show Figures

Graphical abstract

17 pages, 2734 KB  
Article
An Efficient Deep Learning Framework for Optimized Event Forecasting
by Emad Ul Haq Qazi, Muhammad Hamza Faheem, Tanveer Zia, Muhammad Imran and Iftikhar Ahmad
Information 2024, 15(11), 701; https://doi.org/10.3390/info15110701 - 4 Nov 2024
Viewed by 2634
Abstract
There have been several catastrophic events that have impacted multiple economies and resulted in thousands of fatalities, and violence has generated a severe political and financial crisis. Multiple studies have been centered around the artificial intelligence (AI) and machine learning (ML) approaches that [...] Read more.
There have been several catastrophic events that have impacted multiple economies and resulted in thousands of fatalities, and violence has generated a severe political and financial crisis. Multiple studies have been centered around the artificial intelligence (AI) and machine learning (ML) approaches that are most widely used in practice to detect or forecast violent activities. However, machine learning algorithms become less accurate in identifying and forecasting violent activity as data volume and complexity increase. For the prediction of future events, we propose a hybrid deep learning (DL)-based model that is composed of a convolutional neural network (CNN), long short-term memory (LSTM), and an attention layer to learn temporal features from the benchmark the Global Terrorism Database (GTD). The GTD is an internationally recognized database that includes around 190,000 violent events and occurrences worldwide from 1970 to 2020. We took into account two factors for this experimental work: the type of event and the type of object used. The LSTM model takes these complex feature extractions from the CNN first to determine the chronological link between data points, whereas the attention model is used for the time series prediction of an event. The results show that the proposed model achieved good accuracies for both cases—type of event and type of object—compared to benchmark studies using the same dataset (98.1% and 97.6%, respectively). Full article
(This article belongs to the Special Issue Advancements in Healthcare Data Science: Innovations, Challenges and Applications)
►▼ Show Figures

Figure 1

Other

Jump to: Research

31 pages, 1144 KB  
Systematic Review
Smart Contracts, Blockchain, and Health Policies: Past, Present, and Future
by Kenan Kaan Kurt, Meral Timurtaş, Sevcan Pınar, Fatih Ozaydin and Serkan Türkeli
Information 2025, 16(10), 853; https://doi.org/10.3390/info16100853 - 2 Oct 2025
Cited by 6 | Viewed by 5258
Abstract
The integration of blockchain technology into healthcare systems has emerged as a technical solution for enhancing data security, protecting privacy, and improving interoperability. Blockchain-based smart contracts offer reliability, transparency, and efficiency in healthcare services, making them a focal point of many studies. However, [...] Read more.
The integration of blockchain technology into healthcare systems has emerged as a technical solution for enhancing data security, protecting privacy, and improving interoperability. Blockchain-based smart contracts offer reliability, transparency, and efficiency in healthcare services, making them a focal point of many studies. However, challenges such as scalability, regulatory compliance, and interoperability continue to limit their widespread adoption. This study conducts a comprehensive literature review to assess blockchain-driven health data management, focusing on the classification of blockchain-based smart contracts in health policy and the health protocols and standards applicable to blockchain-based smart contracts. This review includes 80 core studies published between 2019 and 2025, identified through searches in PubMed, Scopus, and Web of Science using the PRISMA method. Risk of bias and methodological quality were assessed using the Joanna Briggs Institute tool. The findings highlight the potential of blockchain-enabled smart contracts in health policy management, emphasizing their advantages, limitations, and implementation challenges. Additionally, the research underscores their transformative impact on digital health policies in ensuring data integrity, enhancing patient autonomy, and fostering a more resilient healthcare ecosystem. Recent advancements in quantum technologies are also considered as they present both novel opportunities and emerging threats to the future security and design of healthcare blockchain systems. Full article
(This article belongs to the Special Issue Advancements in Healthcare Data Science: Innovations, Challenges and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-8
Back to TopTop