Machine Learning and Data Science in Healthcare
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 14
Special Issue Editors
Interests: AI; machine learning; data science; digital health
Interests: computer vision and data analytics in the healthcare sector; smart solutions to assist healthcare professionals in anatomical structure segmentation; disease diagnosis
Special Issue Information
Dear Colleagues,
The Special Issue focuses on the transformative role of data-driven approaches in modern medicine and healthcare delivery. It explores innovative applications of data science, including machine learning, artificial intelligence (AI), and big data analytics, to address critical challenges in healthcare.
This Special Issue aims to bridge the gap between technology and medicine by highlighting cutting-edge research and practical implementations. It emphasizes the potential of data science to transform healthcare systems, improve patient outcomes, reduce costs, and drive innovation in medical practice. Topics of interest include (but are not limited to) the following:
- Predictive and Personalised Medicine:
- Leveraging predictive analytics to identify disease risks and tailor treatments for individual patients.
- AI and Machine Learning in Diagnostics:
- Applying AI algorithms to enhance diagnostic accuracy, such as image recognition for medical imaging or natural language processing (NLP) for clinical notes.
- Implementing real-time diagnostic tools to detect early signs of diseases.
- Public Health and Population Management:
- Using data science to model disease outbreaks, optimise resource allocation, and monitor population health trends.
- Enhancing health equity through insights gained from large-scale health data.
- Remote Monitoring and Digital Health:
- Integration of wearable devices and IoT for continuous health monitoring.
- Advancing telemedicine platforms to improve access to care, particularly in remote or underserved areas.
- Clinical Decision Support Systems:
- Designing systems to assist healthcare providers in decision-making by providing evidence-based recommendations.
- Improving patient outcomes through data-driven insights.
Dr. Mohamed Bennasar
Dr. Yutong Xie
Guest Editors
Manuscript Submission Information
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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
- artificial intelligence (AI)
- digital health
- diagnostic tools
- predictive analytics
- clinical decision support systems
- health data visualisation
- machine learning (ML)
- precision medicine
- telemedicine
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