Scholars@Duke
Hayoung Jeong

Selected Publications

Data from the All of Us research program reinforces existence of activity inequality.

Journal Article NPJ digital medicine · January 2025 Large-scale and detailed analyses of activity in the United States (US) remain limited. In this work, we leveraged the comprehensive wearable, demographic, and survey data from the All of Us Research Program, the largest and most diverse population health ... Full text Cite

WatchSleepNet: A Novel Model and Pretraining Approach for Advancing Sleep Staging with Smartwatches

Conference Proceedings of Machine Learning Research · January 1, 2025 Sleep monitoring is essential for assessing overall health and managing sleep disorders, yet clinical adoption of consumer wearables remains limited due to inconsistent performance and scarce open source datasets and transparent codebase. In this study, we ... Cite

Tree-based classification model for Long-COVID infection prediction with age stratification using data from the National COVID Cohort Collaborative.

Journal Article JAMIA Open · December 2024 OBJECTIVES: We propose and validate a domain knowledge-driven classification model for diagnosing post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHRs) data. MATERIALS AND METHODS: We developed ... Full text Link to item Cite

Addressing wearable sleep tracking inequity: a new dataset and novel methods for a population with sleep disorders

Conference Proceedings of Machine Learning Research · January 1, 2024 Sleep is crucial for health, and recent advances in wearable technology and machine learning offer promising methods for monitoring sleep outside the clinical setting. However, sleep tracking using wearables is challenging, particularly for those with irre ... Cite

Building an open-source community to enhance autonomic nervous system signal analysis: DBDP-autonomic.

Journal Article Frontiers in digital health · January 2024 Smartphones and wearable sensors offer an unprecedented ability to collect peripheral psychophysiological signals across diverse timescales, settings, populations, and modalities. However, open-source software development has yet to keep pace with rapid ad ... Full text Cite

Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients.

Journal Article Sci Rep · September 20, 2023 Unsupervised clustering of intensive care unit (ICU) medications may identify unique medication clusters (i.e., pharmacophenotypes) in critically ill adults. We performed an unsupervised analysis with Restricted Boltzmann Machine of 991 medications profile ... Full text Link to item Cite

Neurological Outcome Prediction After Cardiac Arrest: A Multi-Level Deep Learning Approach with Feature and Decision Fusion

Conference Computing in Cardiology · January 1, 2023 Cardiac arrest leads to complex neurological outcomes, demanding accurate predictions to guide post-arrest care. Using the International Cardiac Arrest Research Consortium (I-CARE) dataset, we developed models to discern between 'good' and 'poor' neurologi ... Full text Cite

Pivotal challenges in artificial intelligence and machine learning applications for neonatal care.

Journal Article Semin Fetal Neonatal Med · October 2022 Clinical decision support systems (CDSS) that are developed based on artificial intelligence and machine learning (AI/ML) approaches carry transformative potentials in improving the way neonatal care is practiced. From the use of the data available from el ... Full text Link to item Cite