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Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study.

JMIR formative research
BACKGROUND: Monitoring symptoms of bipolar disorder (BD) is a challenge faced by mental health services. Speech patterns are crucial in assessing the current experiences, emotions, and thought patterns of people with BD. Natural language processing (...

Calibration and Validation of Machine Learning Models for Physical Behavior Characterization: Protocol and Methods for the Free-Living Physical Activity in Youth (FLPAY) Study.

JMIR research protocols
BACKGROUND: Wearable activity monitors are increasingly used to characterize physical behavior. The development and validation of these characterization methods require criterion-labeled data typically collected in a laboratory or simulated free-livi...

Testing a Machine Learning-Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial.

JMIR research protocols
BACKGROUND: Individuals who are socioeconomically disadvantaged have high smoking rates and face barriers to participating in smoking cessation interventions. Computer-tailored health communication, which is focused on finding the most relevant messa...

Machine Learning Approach to Identifying Empathy Using the Vocals of Mental Health Helpline Counselors: Algorithm Development and Validation.

JMIR formative research
BACKGROUND: This research study aimed to detect the vocal features immersed in empathic counselor speech using samples of calls to a mental health helpline service.

Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer.

Frontiers in endocrinology
RESEARCH QUESTION: Can machine learning models accurately predict the risk of early miscarriage following single vitrified-warmed blastocyst transfer (SVBT)?

Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.

Frontiers in public health
BACKGROUND: Exposure to heavy metals has been implicated in adverse auditory health outcomes, yet the precise relationships between heavy metal biomarkers and hearing status remain underexplored. This study leverages a machine learning framework to i...

Constructing a screening model to identify patients at high risk of hospital-acquired influenza on admission to hospital.

Frontiers in public health
OBJECTIVE: To develop a machine learning (ML)-based admission screening model for hospital-acquired (HA) influenza using routinely available data to support early clinical intervention.

Machine learning model for age related macular degeneration based on pesticides: the National Health and Nutrition Examination Survey 2007-2008.

Frontiers in public health
Age-related macular degeneration (AMD) is the most common cause of irreversible deterioration of vision in older adults. Previous studies have found that exposure to pesticides can lead to a worsening of AMD. In this paper, information on pesticide e...

Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer.

Frontiers in immunology
BACKGROUND: Breast cancer is the most common malignancy in women globally, with significant heterogeneity affecting prognosis and treatment. RNA-binding proteins play vital roles in tumor progression, yet their prognostic potential remains unclear. T...

The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians.

Journal of primary care & community health
BACKGROUND: Artificial intelligence (AI) platforms can potentially enhance clinical decision-making (CDM) in primary care settings. OpenEvidence (OE), an AI tool, draws from trusted sources to generate evidence-based medicine (EBM) recommendations to...