Translational Data Science in Precision Medicine and Healthcare

A special issue of Healthcare (ISSN 2227-9032).

Deadline for manuscript submissions: 30 June 2026 | Viewed by 204

Special Issue Editor


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Guest Editor
Department of Anesthesiology and Perioperative Medicine, Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
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

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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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Published Papers (1 paper)

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Research

15 pages, 906 KB  
Article
Safety and Pharmacogenetics of Oxycodone in Post-Cesarean Analgesia and Breastfeeding Dyads: A Proactive Approach to Precision Medicine
by Snehi Shetal Shah, Hsing-Hua Sylvia Lin, Sauren Baheti, Erin Bundock, Alex Anderson, Rose Barlow, Barkha Patel, Linda Park and Senthilkumar Sadhasivam
Healthcare 2026, 14(1), 93; https://doi.org/10.3390/healthcare14010093 (registering DOI) - 31 Dec 2025
Abstract
Background: The aim of the study is (1) to assess safety of opioids in nursing mothers after cesarean delivery and in breastfed infants and (2) to evaluate the role of CYP2D6 genetics in maternal and infant clinical outcomes after cesarean delivery. Methods [...] Read more.
Background: The aim of the study is (1) to assess safety of opioids in nursing mothers after cesarean delivery and in breastfed infants and (2) to evaluate the role of CYP2D6 genetics in maternal and infant clinical outcomes after cesarean delivery. Methods: A total of 210 mother–infant dyads were enrolled after cesarean delivery. Oxycodone 5 mg orally was administered every 4–6 h as needed as part of a standardized opioid-sparing ERAS protocol. Primary outcomes were opioid-related adverse effects, including maternal respiratory depression (RD) and postoperative nausea and vomiting (PONV) and neonatal composite side effects (i.e., RD monitoring, sedation, and limpness). Results: In total, 77% of mothers received opioids during postpartum hospital stay, none experienced respiratory depression, 13% reported PONV, and composite opioid-related side effects were observed in 13% of neonates. Compared to mothers without opioid consumption, higher in-hospital opioid consumption was borderline significantly associated with a higher risk of neonatal composite side effects (adjusted relative risk, aRR = 3.79; 95%CI: 1.01–14.28; p = 0.07), with a similar trend toward higher risk in maternal PONV (aRR = 2.56; 95%CI: 0.70–9.29; p = 0.36). Mothers with a CYP2D6 ultra-rapid metabolizer phenotype also showed higher rates of PONV and neonatal composite side effects compared with normal or intermediate phenotypes, although these associations were not statistically significant. Conclusions: Higher maternal in-hospital opioid consumption is associated with a higher risk of neonatal composite side effects. Using the lowest effective doses of opioids as needed could reduce the risk of opioid-related side effects in neonates. Preoperative genotyping may help identify mothers and breastfed neonates at increased risk for opioid-related adverse outcomes. Additional studies are needed to evaluate preoperative genotyping and to evaluate the causality of increased neonatal adverse outcomes. Full article
(This article belongs to the Special Issue Translational Data Science in Precision Medicine and Healthcare)
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