Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.4
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). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1225

Special Issue 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 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. 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 1600 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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

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

Research

17 pages, 2734 KiB  
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 661
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

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