Risk Management

Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol.

INTRODUCTION: Propofol is a widely used sedative-hypnotic agent for critically ill patients requiring invasive mechanical ventilation (IMV). Despite its clinical benefits, propofol is associated with increased risks of hypertriglyceridemia. Early identification of patients at risk for propofol-associated hypertriglyceridemia is crucial for optimising sedation strategies and preventing adverse outc...

Method for assessing visual saliency in children with cerebral/cortical visual impairment using generative artificial intelligence.

Cerebral/cortical visual impairment (CVI) is a leading cause of pediatric visual impairment in the United States and other developed countries, and is increasingly diagnosed in developing nations due to improved care and survival of children who are born premature or have other risk factors for CVI. Despite this, there is currently no objective, standardized method to quantify the diverse visual i...

Protocol to infer off-target effects of drugs on cellular signaling using interactome-based deep learning.

Drugs that target specific proteins often have off-target effects. We present a protocol using artificial neural networks to model cellular transcript...

"Three-in-one" Analysis of Proteinuria for Disease Diagnosis through Multifunctional Nanoparticles and Machine Learning.

Urinalysis is one of the predominant tools for clinical testing owing to the abundant composition, sufficient volume, and non-invasive acquisition of ...

The biomedical applications of artificial intelligence: an overview of decades of research.

A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and th...

Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning.

Gram staining has been a frequently used staining protocol in microbiology. It is vulnerable to staining artifacts due to, e.g., operator errors and c...

Use of machine learning in occupational risk communication for healthcare workers: protocol for scoping review.

INTRODUCTION: With the development of technology, the use of machine learning (ML), a branch of computer science that aims to transform computers into...

Evaluations of the Perturbation Resistance of the Deep-Learning-Based Ligand Conformation Optimization Algorithm.

In recent years, the deep learning (DL) technique has rapidly developed and shown great success in scoring the protein-ligand binding affinities. The ...

StUdy of Gestational diabetes And Risk using Electronic Data (SUGARED): a population-based cohort study-study protocol.

INTRODUCTION: The incidence of gestational diabetes mellitus (GDM) in Australia has tripled in the last 20 years. Consequently, over 40 000 pregnancie...

Artificial intelligence in medicine and the negative outcome penalty paradox.

Artificial intelligence (AI) holds considerable promise for transforming clinical diagnostics. While much has been written both about public attitudes...

How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review.

Undergraduate students are often impacted by depression, anxiety, and stress. In this context, machine learning may support mental health assessment. ...

Protocol for artificial intelligence-guided neural control using deep reinforcement learning and infrared neural stimulation.

Closed-loop neural control is a powerful tool for both the scientific exploration of neural function and for mitigating deficiencies found in open-loo...

Improving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.

Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such a...

QUAIDE - Quality assessment of AI preclinical studies in diagnostic endoscopy.

Artificial intelligence (AI) holds significant potential for enhancing quality of gastrointestinal (GI) endoscopy, but the adoption of AI in clinical ...

An explainable deep learning platform for molecular discovery.

Deep learning approaches have been increasingly applied to the discovery of novel chemical compounds. These predictive approaches can accurately model...

Acoustic Sensors data transmission integrity and endurance with IoT-enabled location-aware framework.

Environmental monitoring and disaster mitigation are critical applications of underwater acoustic sensor networks (UASNs). However, UASNs face signifi...

AI-enabled clinical decision support tools for mental healthcare: A product review.

The review seeks to promote transparency in the availability of regulated AI-enabled Clinical Decision Support Systems (AI-CDSS) for mental healthcare...

Inclusive AI for radiology: Optimising ChatGPT-4 with advanced prompt engineering.

This letter responds to the article "Encouragement vs. liability: How prompt engineering influences ChatGPT-4's radiology exam performance," offering ...

Using Machine Learning to Fight Child Acute Malnutrition and Predict Weight Gain During Outpatient Treatment with a Simplified Combined Protocol.

BACKGROUND/OBJECTIVES: Child acute malnutrition is a global public health problem, affecting 45 million children under 5 years of age. The World Healt...

Using Artificial Intelligence to Detect Risk of Family Violence: Protocol for a Systematic Review and Meta-Analysis.

BACKGROUND: Despite the implementation of prevention strategies, family violence continues to be a prevalent issue worldwide. Current strategies to re...