Big data approaches for novel mechanistic insights on sleep and circadian rhythms: A workshop summary

Lawrence Baizer; Regina Bures; Girish Nadkarni; Carolyn Reyes-Guzman; Sweta Ladwa; Brian Cade; Michael Brandon Westover; Jeffrey Durmer; Massimiliano De Zambotti; Manisha Desai; Ankit Parekh; Bing Si; Julio Fernandez-Mendoza; Kelton Minor; Diego R. Mazzotti; Soomi Lee; Dina Katabi; Orsolya Kiss; Adam P. Spira; Jonna Morris; Azizi Seixas; Marianthi Anna Kioumourtzoglou; John F.P. Bridges; Marishka Brown; Lauren Hale; Shaun Purcell

doi:10.1093/sleep/zsaf035

Big data approaches for novel mechanistic insights on sleep and circadian rhythms: A workshop summary

Lawrence Baizer
, Regina Bures
, Girish Nadkarni
, Carolyn Reyes-Guzman
, Sweta Ladwa
, Brian Cade
, Michael Brandon Westover
, Jeffrey Durmer
, Massimiliano De Zambotti
, Manisha Desai
, Ankit Parekh
, Bing Si
, Julio Fernandez-Mendoza
, Kelton Minor
, Diego R. Mazzotti
, Soomi Lee
, Dina Katabi
, Orsolya Kiss
, Adam P. Spira
, Jonna Morris

Azizi Seixas, Marianthi Anna Kioumourtzoglou, John F.P. Bridges, Marishka Brown, Lauren Hale, Shaun Purcell

Program in Public Health

National Institutes of Health
Icahn School of Medicine at Mount Sinai
Harvard University
Sleep and Circadian Science
Science Ouraring Inc.
Stanford University
Arizona State University
Pennsylvania State University
Columbia University
University of Kansas
Massachusetts Institute of Technology
SRI International
Johns Hopkins University
University of Pittsburgh
University of Miami
Ohio State University

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

The National Center on Sleep Disorders Research of the National Heart, Lung, and Blood Institute at the National Institutes of Health hosted a 2-day virtual workshop titled Big Data Approaches for Novel Mechanistic Insights on Disorders of Sleep and Circadian Rhythms on May 2nd and 3rd, 2024. The goals of this workshop were to establish a comprehensive understanding of the current state of sleep and circadian rhythm disorders research to identify opportunities to advance the field by using approaches based on artificial intelligence and machine learning. The workshop showcased rapidly developing technologies for sensitive and comprehensive remote analysis of sleep and its disorders that can account for physiological, environmental, and social influences, potentially leading to novel insights on long-Term health consequences of sleep disorders and disparities of these health problems in specific populations.

Original language	English
Article number	zsaf035
Journal	Sleep
Volume	48
Issue number	6
DOIs	https://doi.org/10.1093/sleep/zsaf035
State	Published - Jun 1 2025

Keywords

artificial intelligence
data science
obstructive sleep apnea
remote monitoring
sleep

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10.1093/sleep/zsaf035

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Baizer, L., Bures, R., Nadkarni, G., Reyes-Guzman, C., Ladwa, S., Cade, B., Westover, M. B., Durmer, J., De Zambotti, M., Desai, M., Parekh, A., Si, B., Fernandez-Mendoza, J., Minor, K., Mazzotti, D. R., Lee, S., Katabi, D., Kiss, O., Spira, A. P., ... Purcell, S. (2025). Big data approaches for novel mechanistic insights on sleep and circadian rhythms: A workshop summary. Sleep, 48(6), Article zsaf035. https://doi.org/10.1093/sleep/zsaf035

@article{cbb9255b53ad4469924aaf2666cde07d,

title = "Big data approaches for novel mechanistic insights on sleep and circadian rhythms: A workshop summary",

abstract = "The National Center on Sleep Disorders Research of the National Heart, Lung, and Blood Institute at the National Institutes of Health hosted a 2-day virtual workshop titled Big Data Approaches for Novel Mechanistic Insights on Disorders of Sleep and Circadian Rhythms on May 2nd and 3rd, 2024. The goals of this workshop were to establish a comprehensive understanding of the current state of sleep and circadian rhythm disorders research to identify opportunities to advance the field by using approaches based on artificial intelligence and machine learning. The workshop showcased rapidly developing technologies for sensitive and comprehensive remote analysis of sleep and its disorders that can account for physiological, environmental, and social influences, potentially leading to novel insights on long-Term health consequences of sleep disorders and disparities of these health problems in specific populations.",

keywords = "artificial intelligence, data science, obstructive sleep apnea, remote monitoring, sleep",

author = "Lawrence Baizer and Regina Bures and Girish Nadkarni and Carolyn Reyes-Guzman and Sweta Ladwa and Brian Cade and Westover, \{Michael Brandon\} and Jeffrey Durmer and \{De Zambotti\}, Massimiliano and Manisha Desai and Ankit Parekh and Bing Si and Julio Fernandez-Mendoza and Kelton Minor and Mazzotti, \{Diego R.\} and Soomi Lee and Dina Katabi and Orsolya Kiss and Spira, \{Adam P.\} and Jonna Morris and Azizi Seixas and Kioumourtzoglou, \{Marianthi Anna\} and Bridges, \{John F.P.\} and Marishka Brown and Lauren Hale and Shaun Purcell",

note = "Publisher Copyright: {\textcopyright} 2025 Published by Oxford University Press on behalf of Sleep Research Society (SRS).",

year = "2025",

month = jun,

day = "1",

doi = "10.1093/sleep/zsaf035",

language = "English",

volume = "48",

journal = "Sleep",

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Baizer, L, Bures, R, Nadkarni, G, Reyes-Guzman, C, Ladwa, S, Cade, B, Westover, MB, Durmer, J, De Zambotti, M, Desai, M, Parekh, A, Si, B, Fernandez-Mendoza, J, Minor, K, Mazzotti, DR, Lee, S, Katabi, D, Kiss, O, Spira, AP, Morris, J, Seixas, A, Kioumourtzoglou, MA, Bridges, JFP, Brown, M, Hale, L & Purcell, S 2025, 'Big data approaches for novel mechanistic insights on sleep and circadian rhythms: A workshop summary', Sleep, vol. 48, no. 6, zsaf035. https://doi.org/10.1093/sleep/zsaf035

TY - JOUR

T1 - Big data approaches for novel mechanistic insights on sleep and circadian rhythms

T2 - A workshop summary

AU - Baizer, Lawrence

AU - Bures, Regina

AU - Nadkarni, Girish

AU - Reyes-Guzman, Carolyn

AU - Ladwa, Sweta

AU - Cade, Brian

AU - Westover, Michael Brandon

AU - Durmer, Jeffrey

AU - De Zambotti, Massimiliano

AU - Desai, Manisha

AU - Parekh, Ankit

AU - Si, Bing

AU - Fernandez-Mendoza, Julio

AU - Minor, Kelton

AU - Mazzotti, Diego R.

AU - Lee, Soomi

AU - Katabi, Dina

AU - Kiss, Orsolya

AU - Spira, Adam P.

AU - Morris, Jonna

AU - Seixas, Azizi

AU - Kioumourtzoglou, Marianthi Anna

AU - Bridges, John F.P.

AU - Brown, Marishka

AU - Hale, Lauren

AU - Purcell, Shaun

PY - 2025/6/1

Y1 - 2025/6/1

N2 - The National Center on Sleep Disorders Research of the National Heart, Lung, and Blood Institute at the National Institutes of Health hosted a 2-day virtual workshop titled Big Data Approaches for Novel Mechanistic Insights on Disorders of Sleep and Circadian Rhythms on May 2nd and 3rd, 2024. The goals of this workshop were to establish a comprehensive understanding of the current state of sleep and circadian rhythm disorders research to identify opportunities to advance the field by using approaches based on artificial intelligence and machine learning. The workshop showcased rapidly developing technologies for sensitive and comprehensive remote analysis of sleep and its disorders that can account for physiological, environmental, and social influences, potentially leading to novel insights on long-Term health consequences of sleep disorders and disparities of these health problems in specific populations.

AB - The National Center on Sleep Disorders Research of the National Heart, Lung, and Blood Institute at the National Institutes of Health hosted a 2-day virtual workshop titled Big Data Approaches for Novel Mechanistic Insights on Disorders of Sleep and Circadian Rhythms on May 2nd and 3rd, 2024. The goals of this workshop were to establish a comprehensive understanding of the current state of sleep and circadian rhythm disorders research to identify opportunities to advance the field by using approaches based on artificial intelligence and machine learning. The workshop showcased rapidly developing technologies for sensitive and comprehensive remote analysis of sleep and its disorders that can account for physiological, environmental, and social influences, potentially leading to novel insights on long-Term health consequences of sleep disorders and disparities of these health problems in specific populations.

KW - artificial intelligence

KW - data science

KW - obstructive sleep apnea

KW - remote monitoring

KW - sleep

UR - https://www.scopus.com/pages/publications/105008128813

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DO - 10.1093/sleep/zsaf035

M3 - Article

C2 - 39945146

AN - SCOPUS:105008128813

SN - 0161-8105

VL - 48

JO - Sleep

JF - Sleep

IS - 6

M1 - zsaf035

ER -

Big data approaches for novel mechanistic insights on sleep and circadian rhythms: A workshop summary

Abstract

Keywords

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