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Automated Sleep Staging in Epilepsy Using Deep Learning on Standard Electroencephalogram and Wearable Data.

Journal of sleep research
Automated sleep staging on wearable data could improve our understanding and management of epilepsy. This study evaluated sleep scoring by a deep learning model on 223 night-sleep recordings from 50 patients measured in the hospital with an electroen...

Temporal interactions between neural proxies for memory recall, negative affect, and emotion regulation in major depression.

Molecular psychiatry
Dysfunction in emotion regulation (ER) and autobiographical memory are components of major depressive disorder (MDD). However, little is known about how they mechanistically interact with mood disturbances in real time. Using machine learning-based n...

Artificial Intelligence Model for Time Series Classification: Prediction of Delayed Balloon Expulsion Test Using High-Resolution Anorectal Manometry Data and Time-Series Integrated Pressurized Volume.

Neurogastroenterology and motility
BACKGROUND: We previously demonstrated the novel concept of using the integrated pressurized volume (IPV) with high-resolution anorectal manometry (HRAM) and found that it was predictive of delayed balloon expulsion (BE) test results. However, previo...

Virtual escape rooms in anatomy education: case studies from two institutions.

Advances in physiology education
Virtual escape rooms (ERs) require learners to solve puzzles and answer riddles while trying to "escape" a digital room. Although the educational merit of such gamified learning activities continues to be realized, guides on the development of ERs ar...

Machine learning approach for dosage individualization of azithromycin in children with community-acquired pneumonia.

British journal of clinical pharmacology
AIMS: The uncertainty about the efficacy and safety of currently used azithromycin dosing regimens in children warrants individualized therapy. The area under the plasma concentration-time curve over 24 h (AUC) of azithromycin correlates best with it...

Early obesity risk prediction via non-dietary lifestyle factors using machine learning approaches.

Clinical obesity
Obesity poses a significant health threat, contributing to the development of noncommunicable diseases (NCDs). Early identification of individuals at higher risk for obesity is crucial for implementing effective prevention strategies. This study expl...

A machine learning model to predict intradialytic hypotension in pediatric continuous kidney replacement therapy.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: Intradialytic hypotension (IDH) is associated with mortality in adults undergoing intermittent hemodialysis, but this relationship is unclear in critically ill children receiving continuous kidney replacement therapy (CKRT). We aim to eva...

Human Phenotype Ontology Annotations for Rare Congenital Conditions: Application to Arthrogryposis Multiplex Congenita.

American journal of medical genetics. Part A
Arthrogryposis multiplex congenita (AMC) represents a large, rare group of congenital conditions. This study addressed major challenges in AMC research posed by the lack of systematic frameworks for data collection and the use of inconsistent termino...

Estimating depression severity in narrative clinical notes using large language models.

Journal of affective disorders
BACKGROUND: Depression treatment guidelines emphasize measurement-based care using patient-reported outcome measures, yet their impact on narrative documentation quality remains underexplored.

AI-driven analyzes of open-ended responses to assess outcomes of internet-based cognitive behavioral therapy (ICBT) in adolescents with anxiety and depression comorbidity.

Journal of affective disorders
OBJECTIVE: Although patients prefer describing their problems using words, mental health interventions are commonly evaluated using rating scales. Fortunately, recent advances in natural language processing (i.e., AI-methods) yield new opportunities ...