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Accelerated chemical shift encoded cardiovascular magnetic resonance imaging with use of a resolution enhancement network.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning network (fast chemical shift encoding [FastCSE]) to accelerate CSE.

Sentiment of Nurses Towards Artificial Intelligence and Resistance to Change in Healthcare Organisations: A Mixed-Method Study.

Journal of advanced nursing
BACKGROUND: Research identified preliminary evidence that artificial intelligence (AI) has emerged as a transformative force in healthcare, revolutionising various aspects of healthcare delivery, from diagnostics to treatment planning. However, integ...

Predicting Mortality in Sepsis-Associated Acute Respiratory Distress Syndrome: A Machine Learning Approach Using the MIMIC-III Database.

Journal of intensive care medicine
BackgroundTo develop and validate a mortality prediction model for patients with sepsis-associated Acute Respiratory Distress Syndrome (ARDS).MethodsThis retrospective cohort study included 2466 patients diagnosed with sepsis and ARDS within 24 h of ...

Suicidal behaviors among high school graduates with preexisting mental health problems: A machine learning and GIS-based study.

The International journal of social psychiatry
BACKGROUND: Suicidal behavior among adolescents with mental health disorders, such as depression and anxiety, is a critical issue. This study explores the prevalence and predictors of past-year suicidal behaviors among Bangladeshi high school graduat...

Prediction of cognitive impairment among Medicare beneficiaries using a machine learning approach.

Archives of gerontology and geriatrics
OBJECTIVE: Developing machine learning (ML) models to predict cognitive impairment among Medicare beneficiaries in the United States.

Risk factors for depression in China based on machine learning algorithms: A cross-sectional survey of 264,557 non-manual workers.

Journal of affective disorders
BACKGROUND: Factors related to depression differ depending on the population studied, and studies focusing on the population of non-manual workers are lacking. Thus, we aimed to identify the risk factors related to depression in non-manual workers in...

Predicting cannabis use moderation among a sample of digital self-help subscribers: A machine learning study.

Drug and alcohol dependence
BACKGROUND: For individuals who wish to reduce their cannabis use without formal help, there are a variety of self-help tools available. Although some are proven to be effective in reducing cannabis use, effect sizes are typically small. More insight...

Prediction of anhedonia in patients with first-episode schizophrenia using a Wavelet-ALFF-based Support vector regression model.

Neuroscience
Anhedonia is one of the core features of the negative symptoms of schizophrenia and can be extremely burdensome. Our study applied resting-state functional magnetic resonance imaging (fMRI)-based support vector regression (SVR) to predict anhedonia i...

Integrating Laser-Induced Breakdown Spectroscopy and Ensemble Learning as Minimally Invasive Optical Screening for Diabetes.

Applied spectroscopy
Diabetes mellitus is a prevalent chronic disease necessitating timely identification for effective management. This paper introduces a reliable, straightforward, and efficient method for the minimally invasive identification of diabetes mellitus thro...

Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke but associated with high mortality rates. Hematoma expansion (HE) is an increase in the bleeding that affects 30%-38% of hemorrhagic stroke patients. I...