Sleep Disorders

Novel bayesian nonparametric unsupervised learning approach to precision symptom management in cancer survivors: a re-analysis of a comparative effectiveness trial.

Cancer survivors often experience multiple cooccurring symptoms such as insomnia, pain, fatigue, and anxiety; yet conventional analyses in symptom science typically analyze symptoms one at a time and thus overlook putative clusters of shared symptom experiences. We applied a novel machine learning approach to supporting tailored symptom management to cooccurring symptoms. Bayesian nonparametric (B...

Self-Supervised Speech Representations for Sleep Apnea Severity Prediction.

OBJECTIVE: Obstructive sleep apnea (OSA) is a prevalent yet underdiagnosed condition characterized by repetitive upper airway obstruction during sleep. Current gold standard diagnostic standards rely on polysomnography (PSG), which is resource-intensive. Since upper airway characteristics impact both OSA and speech production, speech processing has emerged as a promising alternative for OSA screen...

Deep learning-based automatic adenoid segmentation and a novel volume-based index for adenoid hypertrophy assessment.

BACKGROUND: Adenoid hypertrophy is a common cause of pediatric obstructive sleep apnea (OSA), which can impair cognitive development and affect cranio...

Neural signatures of major depressive disorder with comorbid insomnia: Low gamma PoCG.R-INS.L connectivity as a potential biomarker.

BACKGROUND: Insomnia is a common and distressing symptom in major depressive disorder (MDD). However, research focusing on the neurobiological mechani...

Sleep apnoea and its consequences: from animal models to precision medicine.

A European Respiratory Society research seminar entitled "Sleep Apnoea and Its Consequences: From Animal Models to Precision Medicine" was held in Jan...

Subjective sleep quality predict clinical pain severity more strongly than polysomnographic parameters: Machine learning findings from a cross-sectional study.

Poor sleep quality amplifies pain experience. However, it remains uncertain regarding whether subjectively or physiologically assessed sleep parameter...

Korean longitudinal study on digitally optimized mental healthcare: a cohort profile.

The Korean Longitudinal Study on Digitally Optimized Mental Healthcare is an innovative multicenter trial-ready cohort study. It aims to develop a dig...

Combining network pharmacology, machine learning, molecular docking, molecular simulation dynamics and experimental validation to explore the mechanism of Zhenwu decoction in treating major depression through TNF-α pathways.

BACKGROUND: Major depressive disorder (MDD) is a severe psychophysiological condition characterized by cognitive decline, low energy, weight loss, ins...

Refining Sleep-Disordered Breathing Annotations Across Multiple Public Sleep Study Datasets.

Polysomnography annotations in Sleep Heart Health Study (SHHS), Osteoporotic Fractures in Men Study (MrOS), and Multi-Ethnic Study of Atherosclerosis ...

Diagnostic modalities in pediatric obstructive sleep apnea: A review article.

BACKGROUND: Pediatric obstructive sleep apnea (OSA) affects ∼3 % of children and causes adverse neurocognitive, behavioral, and cardiovascular outcome...

How Technology Can Increase Access to Sleep Health Care.

Sleep disorders, like insomnia and sleep apnea, affect millions globally and negatively impact health and well-being. Traditional barriers such as geo...

Exploring functional insights into the human gut microbiome via the structural proteome.

The human gut microbiome contains numerous proteins whose functions remain elusive yet are pivotal to host health. Sequence-based methods often falter...

Sleep stage classification from ECG using machine learning: Evaluating the impact of signal duration.

Sleep stage flagging is critical for diagnosing conditions like insomnia, sleep apnea, and narcolepsy. Traditional methods rely on time-intensive manu...

POLR2C, HIF1A, CD4, and CREB1 as the identified key regulators in geriatric insomnia: A comprehensive approach using systems biology and machine learning methods.

BACKGROUND: Insomnia is a prevalent sleep disorder in older adults with an unknown pathogenesis. This study aims to develop a predictive molecular sig...

Development of explainable machine learning-based prediction models of depression with mild and severe insomnia in middle-aged and elderly Chinese people.

BACKGROUND: The risk of depression is significantly elevated in middle-aged and older adults with insomnia; however, the pathways between mild and sev...

Uncovering predictors of bipolar II conversion to bipolar I: A machine learning analysis of national health records in Taiwan.

BACKGROUND: Bipolar II disorder (BD-II) can progress to bipolar I disorder (BD-I), carrying profound psychosocial consequences. However, limited resea...

Dynamic Functional Network Connectivity Patterns Distinguish Neurobiological Substrates of Narcolepsy Type 1 and Idiopathic Hypersomnia: Potential Biomarkers From Resting-State fMRI.

This study aimed to explore dynamic functional network connectivity (dFNC) differences between narcolepsy type 1 (NT1), idiopathic hypersomnia (IH), a...

Research experience and career factors in relation to mental health problems: Prevalence, risk factors, and machine learning-based predictive estimates.

BACKGROUND: Mental health issues, including depression, anxiety, and insomnia, are increasingly prevalent among university students and graduates, esp...

Predicting Sleep and Sleep Stage in Children Using Actigraphy and Heartrate via a Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation.

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to p...

Novel technologies for REM sleep behavior disorder detection for home screening in Parkinson's disease and related alpha-synucleinopathies.

Isolated REM sleep behavior disorder (iRBD) is a prodromal marker of Parkinson's disease (PD) and related alpha-synucleinopathies. Identification of R...