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A Machine Learning Method for Allocating Scarce COVID-19 Monoclonal Antibodies.

JAMA health forum
IMPORTANCE: During the COVID-19 pandemic, the effective distribution of limited treatments became a crucial policy goal. Yet, limited research exists using electronic health record data and machine learning techniques, such as policy learning trees (...

Prediction of childbearing tendency in women on the verge of marriage using machine learning techniques.

Scientific reports
The declining fertility rate and increasing marriage age among girls pose challenges for policymakers, leading to issues such as population decline, higher social and economic costs, and reduced labor productivity. Using machine learning (ML) techniq...

Using recurrent neural network to estimate irreducible stochasticity in human choice behavior.

eLife
Theoretical computational models are widely used to describe latent cognitive processes. However, these models do not equally explain data across participants, with some individuals showing a bigger predictive gap than others. In the current study, w...

Using machine learning to predict judgments on Western visual art along content-representational and formal-perceptual attributes.

PloS one
Art research has long aimed to unravel the complex associations between specific attributes, such as color, complexity, and emotional expressiveness, and art judgments, including beauty, creativity, and liking. However, the fundamental distinction be...

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.

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...

A Generalisable Heartbeat Classifier Leveraging Self-Supervised Learning for ECG Analysis During Magnetic Resonance Imaging.

IEEE journal of biomedical and health informatics
Electrocardiogram (ECG) is acquired during Magnetic Resonance Imaging (MRI) to monitor patients and synchronize image acquisition with the heart motion. ECG signals are highly distorted during MRI due to the complex electromagnetic environment. Autom...

Classification of Three Anesthesia Stages Based on Near-Infrared Spectroscopy Signals.

IEEE journal of biomedical and health informatics
Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In this study, different anesthesia stages were classified using near-infrared spectroscopy (NIRS) signals with machine learning. The cerebral hemodynami...