Editorial: Unleashing the power of large data: models to improve individual health outcomes
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fdgth-7-1668543.pdf
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78.33 KB
Format
Adobe PDF
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Author(s)
Aickelin, Uwe
Jeong, Hyewon
Kanjilal, Sanjat
Yu, Sherry
Kothakonda, Akshay
Date Issued
August 4, 2025
Journal
Frontiers in Digital Health
Publisher
Frontiers Media SA
Citation
Jeong H, Kanjilal S, Yu SH and Kothakonda A (2025) Editorial: Unleashing the power of large data: models to improve individual health outcomes. Front. Digit. Health 7:1668543.
Version
Final published version
Abstract
The digital transformation of healthcare has unleashed unprecedented volumes of data from electronic health records (EHRs), wearables, social media, and beyond. These big data assets, coupled with advances in artificial intelligence (AI) and machine learning (ML), promise to drive precision health. However, key challenges remain in data quality, integration, and model interpretability. This Research Topic brings together interdisciplinary research that illustrates how data-driven models can improve health outcomes. They span efforts to ensure high-quality EHR-based research, novel ML applications for mental health and telehealth, data-driven clinical decision support, and techniques for making complex models interpretable and transparent.
Subjects
digital health data
machine learning
personalized medicine
big data
biomedical signal
electronic health record -(EHR)
public health
predictive modeling
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/fdgth.2025.1668543
