Research - EnsoData

Sleep Architecture Associations with Brain Age: A Multi-Site Model Validation

This research study evaluates large multi-site datasets and assesses the relationship of N3/REM sleep duration with the predicted brain age.

Deep Learning to Predict PAP Adherence in Obstructive Sleep Apnea

In this study, EnsoData shows how ML algorithms based on PAP usage can predict future adherence, offering potential for personalized treatment decisions and preemptive interventions when upcoming non-adherence is predicted.

Artificial Intelligence to Aid in Diagnosis of Type I Narcolepsy

This research study demonstrated that Machine Learning methods can automatically detect Type I Narcolepsy using in PSG-EEG with promising degrees of accuracy.

Use of Artificial Intelligence for Early Characterization of Patients with RBD

This study demonstrates the ability of AI approaches produced high specificity and moderate sensitivity for REM Behavior Disorder and the potential to expand early detection and diagnosis of RBD.

Deep Learning Classification of Future PAP Adherence based on CMS and other Adherence Criteria

This research study shows how AI can deliver strong predictive performance for PAP adherence within the first few weeks of therapy, enabling early PAP intervention or transition to alternative therapies sooner in the process and improving patient outcomes.

Polysomnography following an indeterminate HSAT: Low Compliance with AASM Guidelines

In this study, we evaluate whether patients are likely to comply with receiving a follow-up PSG following an indeterminate HSAT to rule out any presence of OSA and assess the demographic characteristics of individuals who are more likely to follow the AASM guidelines.

Evaluation of Healthcare Insurance Claims Record based Artificial Intelligence Screening Tools for Undiagnosed Obstructive Sleep

This research examined the feasibility for machine learning algorithms to improve upon screening for obstructive and central sleep apnea (SA) at the population health level using existing health insurance claims data.

Impact of OSA Therapy on Healthcare Costs: Actuarial Analysis of OSA Prevalence, Therapy Adherence, Co-morbidity, and Costs in a Large Medicare Population Cohort

This study examines the relationship between OSA Therapy and other key healthcare economics, including the prevalence of undiagnosed OSA, rate of diagnosed patients not starting continuous positive airway pressure (CPAP) therapy, spectrum of CPAP treatment adherence, and effects of concurrent co-morbidity.

How will artificial intelligence (AI) advance sleep medicine?

This research abstract addresses various components and methods deployed in AI and covers examples of how AI is used to screen, endotype, diagnose, and treat sleep disorders.