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Construction and temporal external validation of interpretable machine-learning models for predicting tigecycline-associated hypofibrinogenemia.

European journal of clinical pharmacology
BACKGROUND AND PURPOSE: There is a paucity of available clinical tools with which to accurately predict the risk of tigecycline-associated hypofibrinogenemia, an adverse reaction with a high incidence and serious consequences. This study aimed to dev...

Vulnerabilities of feature clustering in EIT radiomics.

Computers in biology and medicine
BACKGROUND: We aimed to determine whether unsupervised machine learning was able to discover latent and possibly clinically-relevant clusters, hidden in dynamic electrical impedance tomography (EIT) images within a population of mechanically ventilat...

Stress detection using the phase controlled Bi-channel adaptive features from the brain EEG signals.

Computers in biology and medicine
This work proposes a stress classification system from the electroencephalogram (EEG) signals collected from the stress subjects. The scheme extracts the phase-controlled Bi-channel adaptive features using a pair of EEG signals. The proposed adaptive...

What Drives Microplastic Exposure in Human Blood and Feces? Machine Learning Reveals Potential Key Influencing Factors.

Environmental science & technology
Microplastics are pervasive environmental pollutants, making human exposure unavoidable. Although previous studies have detected microplastics in human blood and feces, these investigations were limited by small sample sizes and key contributors to m...

Artificial intelligence guidance in ethically challenging clinical scenarios in child and adolescent psychiatry: a qualitative study in the context of Turkiye.

BMC medical ethics
BACKGROUND: Ethical decision-making in child and adolescent psychiatry (CAP) is inherently complex, shaped by developmental vulnerability, evolving autonomy, and competing responsibilities to patients, families, and the legal system. Clinicians often...

How perceived stress and social support shape non-communicable disease risks beyond traditional factors: a machine learning perspective.

BMC public health
BACKGROUND: Psychosocial factors such as perceived stress and social support have been increasingly recognized as significant contributors to non-communicable diseases (NCDs). However, their predictive value in comparison to traditional risk factors ...

Evaluation of Few-Shot AI-Generated Feedback on Case Reports in Physical Therapy Education: Mixed Methods Study.

JMIR medical education
BACKGROUND: While artificial intelligence (AI)-generated feedback offers significant potential to overcome constraints on faculty time and resources associated with providing personalized feedback, its perceived usefulness can be undermined by algori...

Predicting Ultra-High Risk Outcomes Using Linguistic and Acoustic Measures From High-Risk Social Challenge Recordings: mHealth Longitudinal Cohort Exploratory Study.

JMIR formative research
BACKGROUND: Early detection of individuals at ultra-high risk (UHR) for psychosis is critical for timely intervention and improving clinical outcomes. However, current UHR assessments, which rely heavily on psychometric tools, often suffer from low s...

Artificial Intelligence-Enhanced Multi-Algorithm R Shiny Application for Predictive Modeling and Analytics: Case Study of Alzheimer Disease Diagnostics.

JMIR aging
BACKGROUND: Artificial intelligence (AI) has demonstrated superior diagnostic accuracy compared with medical practitioners, highlighting its growing importance in health care. SMART-Pred (Shiny Multi-Algorithm R Tool for Predictive Modeling) is an in...

Physician Perspectives on the Impact of Artificial Intelligence on the Therapeutic Relationship in Mental Health Care: Qualitative Study.

JMIR mental health
BACKGROUND: The therapeutic relationship is a professional partnership between clinicians and patients that supports open communication and clinical decision-making. This relationship is critical to the delivery of effective mental health care. The i...