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Adipose tissue gene expression and longitudinal clinical phenotypes are early biomarkers of lipid-regulating drug usage.

Scientific reports
Cardiovascular disease progression is characterised by the dysregulation of lipid metabolism and pro-atherogenic effects of adipose tissue signalling. Recent findings from the analysis of transcriptomic data in bulk tissue has enabled these insights ...

Synthetic data generation method improves risk prediction model for early tumor recurrence after surgery in patients with pancreatic cancer.

Scientific reports
Pancreatic cancer is aggressive with high recurrence rates, necessitating accurate prediction models for effective treatment planning, particularly for neoadjuvant chemotherapy or upfront surgery. This study explores the use of variational autoencode...

Using deep learning to predict internalizing problems from brain structure in youth.

Translational psychiatry
Internalizing problems (e.g., anxiety and depression) are associated with a wide range of adverse outcomes. While some predictors of internalizing problems are known (e.g., their frequent co-occurrence with neurodevelopmental (ND) conditions), the bi...

Deep Learning Radiomics Model Based on Computed Tomography Image for Predicting the Classification of Osteoporotic Vertebral Fractures: Algorithm Development and Validation.

JMIR medical informatics
BACKGROUND: Osteoporotic vertebral fractures (OVFs) are common in older adults and often lead to disability if not properly diagnosed and classified. With the increased use of computed tomography (CT) imaging and the development of radiomics and deep...

Development and Validation of a Large Language Model-Based System for Medical History-Taking Training: Prospective Multicase Study on Evaluation Stability, Human-AI Consistency, and Transparency.

JMIR medical education
BACKGROUND: History-taking is crucial in medical training. However, current methods often lack consistent feedback and standardized evaluation and have limited access to standardized patient (SP) resources. Artificial intelligence (AI)-powered simula...

A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation.

JMIR medical informatics
BACKGROUND: Identifying neuroinfectious disease (NID) cases using International Classification of Diseases billing codes is often imprecise, while manual chart reviews are labor-intensive. Machine learning models can leverage unstructured electronic ...

A Robot-Delivered Training Program to Improve Children's Mental Health and Resilience in Dutch Primary Schools: Pilot Intervention Study.

JMIR formative research
BACKGROUND: Mental health problems often start at an early age and can persist into adulthood, leading to physical and mental health problems such as substance abuse, sleep problems, depressive disorders, and suicidal tendencies. Therefore, it is imp...

Mapping Vaccine Sentiment by Analyzing Spanish-Language Social Media Posts and Survey-Based Public Opinion: Dual Methods Study.

JMIR infodemiology
BACKGROUND: The internet and social media have been considered useful platforms for obtaining health information. However, critical and erroneous content about vaccines on social media has been associated with vaccination delays and refusal.

Recognizing Skateboard and Kickboard Commuting Behaviors Using Activity Trackers: Feasibility Study Using Machine Learning Approaches.

JMIR formative research
BACKGROUND: Active commuting, such as skateboarding and kickboarding, is gaining popularity as an alternative to traditional modes of transportation such as walking and cycling. However, current activity trackers and smartphones, which rely on accele...

Natural Language Processing and Coding for Detecting Bleeding Events in Discharge Summaries: Comparative Cross-Sectional Study.

JMIR medical informatics
BACKGROUND: Bleeding adverse drug events (ADEs), particularly among older inpatients receiving antithrombotic therapy, represent a major safety concern in hospitals. These events are often underdetected by conventional rule-based systems relying on s...