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Deep Learning-Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study.

Journal of medical Internet research
BACKGROUND: In-hospital cardiac arrest (IHCA) is a severe and sudden medical emergency that is characterized by the abrupt cessation of circulatory function, leading to death or irreversible organ damage if not addressed immediately. Emergency depart...

The use of artificial intelligence in psychotherapy: development of intelligent therapeutic systems.

BMC psychology
BACKGROUND: The increasing demand for psychotherapy and limited access to specialists underscore the potential of artificial intelligence (AI) in mental health care. This study evaluates the effectiveness of the AI-powered Friend chatbot in providing...

Preoperative diagnosis of meningioma sinus invasion based on MRI radiomics and deep learning: a multicenter study.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVE: Exploring the construction of a fusion model that combines radiomics and deep learning (DL) features is of great significance for the precise preoperative diagnosis of meningioma sinus invasion.

Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models.

BMC pregnancy and childbirth
OBJECTIVE: To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of m...

Estimation of Ganciclovir Exposure in Adults Transplant Patients by Machine Learning.

The AAPS journal
INTRODUCTION: Valganciclovir, a prodrug of ganciclovir (GCV), is used to prevent cytomegalovirus infection after transplantation, with doses adjusted based on creatinine clearance (CrCL) to target GCV AUC0-24 h of 40-60 mg*h/L. This sometimes leads t...

Insights into the temporal dynamics of identifying problem gambling on an online casino: A machine learning study on routinely collected individual account data.

Journal of behavioral addictions
BACKGROUND AND AIMS: The digitalization of gambling provides unprecedented opportunities for early identification of problem gambling, a well-recognized public health issue. This study aimed to advance current practices by employing advanced machine ...

Machine learning analysis of cortical activity in visual associative learning tasks with differing stimulus complexity.

Physiology international
Associative learning tests are cognitive assessments that evaluate the ability of individuals to learn and remember relationships between pairs of stimuli. The Rutgers Acquired Equivalence Test (RAET) is an associative learning test that utilizes ima...

Contribution of Structure Learning Algorithms in Social Epidemiology: Application to Real-World Data.

International journal of environmental research and public health
Epidemiologists often handle large datasets with numerous variables and are currently seeing a growing wealth of techniques for data analysis, such as machine learning. Critical aspects involve addressing causality, often based on observational data,...

Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology.

Tomography (Ann Arbor, Mich.)
BACKGROUND: Temporomandibular joint (TMJ) disorders are a significant cause of orofacial pain. Artificial intelligence (AI) has been successfully applied to other imaging modalities but remains underexplored in ultrasonographic evaluations of TMJ.

AI-Powered Analysis of Weight Loss Reports from Reddit: Unlocking Social Media's Potential in Dietary Assessment.

Nutrients
: The increasing use of social media for sharing health and diet experiences presents new opportunities for nutritional research and dietary assessment. Large language models (LLMs) and artificial intelligence (AI) offer innovative approaches to anal...