Translational Data Science in Precision Medicine and Healthcare
A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 29
Special Issue Editor
Interests: large real-world electronic health records; causal inference; clinical trials; precision medicine; pharmacogenomics; pain management; perioperative medicine
Special Issue Information
Dear Colleagues,
We are pleased to announce a new Special Issue of Healthcare titled “Translational Data Science in Precision Medicine and Healthcare”. This Issue will focus on the integration of translational analytics, causal inference, machine learning, artificial intelligence, and clinical informatics to support precision diagnostics, therapeutics, and health delivery innovations.
Translational data science serves as a bridge between computational methods and clinical impact, leveraging real-world data to generate actionable insights in medicine. The field has witnessed rapid growth, particularly with the increasing availability of multi-omics, EHRs, wearable technologies, and population health datasets. There is an urgent need to explore how these approaches can improve population health, patient outcomes, enhance clinical workflows, and guide individualized treatment strategies.
This Special Issue aims to highlight cutting-edge methods, applied studies, and implementation frameworks that advance precision medicine through data-driven insights.
We welcome original research articles, reviews, and case studies that explore, but are not limited to, the following topics:
- Clinical trial design leveraging translational analytics;
- Causal inference methods for treatment effect estimation and decision-making;
- Applications of AI/ML in precision diagnostics and treatment planning;
- Data integration across omics, imaging, and clinical records;
- Predictive modeling and risk stratification tools;
- Digital phenotyping and individualized health trajectories;
- Real-world evidence generation using EHR and registry data;
- Explainable AI (XAI) in clinical decision-making;
- Implementation and evaluation of informatics tools in healthcare systems;
- Ethical and regulatory considerations in data science for healthcare.
We look forward to your valuable contributions to this timely and impactful Special Issue.
Warm regards,
Dr. Hsing-Hua Sylvia Lin
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. Healthcare is an international peer-reviewed open access semimonthly 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 2700 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
- translational data science
- precision medicine
- causal inference
- artificial intelligence
- machine learning
- clinical informatics
- predictive analytics
- real-world evidence
- multi-omics integration
- personalized healthcare
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