Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Yoav Nygate"'
Autor:
Thomas J Vanasse, Samuel Rusk, Yoav Nygate, Chris Fernandez, Jiaxiao M Shi, Jessica Arguelles, Matthew T Klimper, Emerson Wickwire, Nathaniel F Watson, Dennis Hwang
Publikováno v:
SLEEP. 46:A40-A41
Introduction Machine Learning (ML) can draw upon complex patient data to predict current and future health status, but the best performing ML models are often difficult to interpret. One such ML-defined health marker is the brain age index (BAI), the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e461206ea48d3ea3428324b4daea02eb
https://doi.org/10.1093/sleep/zsad077.0090
https://doi.org/10.1093/sleep/zsad077.0090
Autor:
Samuel Rusk, Yoav Nygate, Chris Fernandez, Jiaxiao M Shi, Jessica Arguelles, Matthew T Klimper, Nathaniel F Watson, Robert Stretch, Michelle Zeidler, Anupamjeet Sekhon, Kendra Becker, Joseph Kim, Dennis Hwang
Publikováno v:
SLEEP. 46:A206-A206
Introduction Improving positive airway pressure (PAP) adherence is crucial to sleep apnea therapy success. Although behavioral interventions may be deployed to increase PAP adherence, operationalization remains an ongoing clinical challenge. Treatmen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e9d0d0fa0f1f131f67ec3a463bd6c888
https://doi.org/10.1093/sleep/zsad077.0463
https://doi.org/10.1093/sleep/zsad077.0463
Autor:
Yoav Nygate, Samuel Rusk, Chris Fernandez, Zac Winzurk, Emerson Wickwire, Emmanuel Mignot, Nathaniel F Watson
Publikováno v:
SLEEP. 46:A252-A252
Introduction Accurate diagnosis of Type 1 narcolepsy (T1N) is cumbersome – involving clinical, biological, and electrophysiological components. Multiple sleep latency tests (MSLT) are central to diagnosis; however, current medications, sleep schedu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fcd321e078dbebf36f942fb3f6002223
https://doi.org/10.1093/sleep/zsad077.0574
https://doi.org/10.1093/sleep/zsad077.0574
Autor:
Samuel Rusk, Yoav Nygate, Chris Fernandez, Jiaxiao M Shi, Jessica Arguelles, Matthew T Klimper, Nathaniel F Watson, Robert Stretch, Michelle Zeidler, Anupamjeet Sekhon, Kendra Becker, Joseph Kim, Dennis Hwang
Publikováno v:
SLEEP. 46:A206-A206
Introduction Machine Learning (ML) algorithms to predict Positive Airway Pressure (PAP) adherence may support personalized clinical management. Models were developed to predict adherence at various time-points after PAP initiation and in moving time
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8341ec0a30a9fb310802c0ee5edc04cb
https://doi.org/10.1093/sleep/zsad077.0464
https://doi.org/10.1093/sleep/zsad077.0464
Autor:
Chris Fernandez, Sam Rusk, Nick Glattard, Fred Turkington, Yoav Nygate, Mark Kaiser, Jen McClurg, Maggie Richard, Ian Duncan, Nathaniel Watson
Publikováno v:
Sleep. 45:A31-A32
Introduction Research studying the economics of OSA therapy faces confounds including the prevalence of undiagnosed OSA, rate of diagnosed patients declining therapy, spectrum of treatment adherence, and effects of concurrent co-morbidity. We provide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0380f0ee203dccd4337ddbc7f5bcafc7
https://doi.org/10.1093/sleep/zsac079.066
https://doi.org/10.1093/sleep/zsac079.066
Autor:
Sam Rusk, Fred Turkington, Chris Fernandez, Yoav Nygate, Nick Glattard, Melania Abrahamian, Tom Vanasse, Dana Richardson, Tim Bartholow, Nathaniel Watson
Publikováno v:
Sleep. 45:A316-A316
Introduction Healthcare insurance claims data contain an unrecognized wealth of structured data that can be leveraged to investigate epidemiologic and economic relationships in health and disease. We studied the feasibility for machine learning algor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5df44b9912c259e49f7fd439d459978d
https://doi.org/10.1093/sleep/zsac079.718
https://doi.org/10.1093/sleep/zsac079.718
Autor:
Yoav Nygate, Sam Rusk, Fred Turkington, Chris Fernandez, Nick Glattard, Matt Sprague, Zac Winzurk, Nathaniel Watson
Publikováno v:
Sleep. 45:A166-A166
Introduction Home sleep apnea test (HSAT) provides a low risk, cost-effective, and convenient diagnostic test for obstructive sleep apnea (OSA) in adult patients. However, in situations where HSAT are inconclusive, technically inadequate, or produce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c4e79a42c7db50ed1628655afcd37f79
https://doi.org/10.1093/sleep/zsac079.367
https://doi.org/10.1093/sleep/zsac079.367
Publikováno v:
Sleep. 44:A168-A169
Introduction The Photoplesthymogram (PPG) raw waveform is the basis for both the pulse rate and oximetry during polysomnography (PSG) and Home Sleep Apnea Tests (HSAT). The PPG has also recently become ubiquitous as a basis of continuous measurement
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af6a6244ba0bd9fc64610e53803a6db3
https://doi.org/10.1093/sleep/zsab072.425
https://doi.org/10.1093/sleep/zsab072.425
Publikováno v:
Sleep. 44:A167-A168
Introduction The STOP-Bang is a concise, simple and widely adopted obstructive sleep apnea (OSA) screening tool. However, it has limited predictive ability and is susceptible to subjective reporting bias. Artificial Intelligence (AI) methodologies ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d66ad1e8565ff269e28f35f76d7765e6
https://doi.org/10.1093/sleep/zsab072.422
https://doi.org/10.1093/sleep/zsab072.422
Autor:
Nick Glattard, Yoav Nygate, Chris Fernandez, Jessica Arguelles, Dennis Hwang, Sam Rusk, Jiaxiao Shi
Publikováno v:
Sleep
Introduction Current approach to processing polysomnography is labor intensive and produces metrics that are poor at identifying obstructive sleep apnea (OSA) phenotypes necessary to enhance personalized care. We describe our approach to utilize Dyna
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c4d8def16e5e6587a581f0510a95955
https://doi.org/10.1093/sleep/zsab072.408
https://doi.org/10.1093/sleep/zsab072.408