ArticleNPJ digital medicine2022
A computational framework for discovering digital biomarkers of glycemic control.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Seasonal, weekly, and individual variations in long-term use of wearable medical devices for diabetes management.Scientific reports · 2025Article
- A perspective on harmonizing diabetes management datasets.Data in brief · 2025Article
- An AI-based module for interstitial glucose forecasting enabling a "Do-It-Yourself" application for people with type 1 diabetes.Frontiers in digital health · 2025Article
- Mealtime prediction using wearable insulin pump data to support diabetes management.Scientific reports · 2024Article
- Machine Learning Models for Predicting Mortality in Critically Ill Patients with Sepsis-Associated Acute Kidney Injury: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers.JAMIA open · 2024Review
- Definitions of digital biomarkers: a systematic mapping of the biomedical literature.BMJ health & care informatics · 2024Review
- Reduced portal vein blood flow velocity in acute fatty liver of pregnancy.Frontiers in medicine · 2024Article
- Understanding temporal changes and seasonal variations in glycemic trends using wearable data.Science advances · 2023Article
- Solving the explainable AI conundrum by bridging clinicians' needs and developers' goals.NPJ digital medicine · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
Digital biomarkers can radically transform the standard of care for chronic conditions that are complex to manage. In this work, we propose a scalable computational framework for discovering digital biomarkers of glycemic control. As a feasibility study, we leveraged over 79,000 days of digital data to define objective features, model the impact of each feature, classify glycemic control, and identify the most impactful digital biomarkers. Our research shows that glycemic control varies by age group, and was worse in the youngest population of subjects between the ages of 2-14. In addition, digital biomarkers like prior-day time above range and prior-day time in range, as well as total daily bolus and total daily basal were most predictive of impending glycemic control. With a combination of the top-ranked digital biomarkers, we achieved an average F1 score of 82.4% and 89.7% for classifying next-day glycemic control across two unique datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.