Public Health & Policy

Clinical Trials

Latest AI and machine learning research in clinical trials for healthcare professionals.

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Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Large randomized trials in sepsis have generally failed to find effective novel treatments. This is ...

A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches.

INTRODUCTION: The identification of a new adverse event (AE) caused by a drug product is one of the ...

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction.

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting dr...

Machine Teaching Allows for Rapid Development of Automated Systems for Retinal Lesion Detection From Small Image Datasets.

Machine teaching, a machine learning subfield, may allow for rapid development of artificial intelli...

Machine Learning-Based Approach to Predict Last-Minute Cancellation of Pediatric Day Surgeries.

The last-minute cancellation of surgeries profoundly affects patients and their families. This resea...

Automatic Skeleton Segmentation in CT Images Based on U-Net.

Bone metastasis, emerging oncological therapies, and osteoporosis represent some of the distinct cli...

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit ma...

AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial.

The early identification of vulnerable patients has the potential to improve outcomes but poses a su...

Leveraging machine learning: Covariate-adjusted Bayesian adaptive randomization and subgroup discovery in multi-arm survival trials.

Clinical trials evaluate the safety and efficacy of treatments for specific diseases. Ensuring these...

Current perspectives on the use of artificial intelligence in critical patient safety.

Intensive Care Units (ICUs) have undergone enhancements in patient safety, and artificial intelligen...

Implementation of artificial intelligence-based computer vision model in laparoscopic appendectomy: validation, reliability, and clinical correlation.

BACKGROUND: Application of artificial intelligence (AI) in general surgery is evolving. Real-world i...

Predicting clinical outcomes of SARS-CoV-2 infection during the Omicron wave using machine learning.

The Omicron SARS-CoV-2 variant continues to strain healthcare systems. Developing tools that facilit...

Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks.

This paper discusses the impact of artificial intelligence (AI) on occupational health and safety. A...

To trust or not to trust: evaluating the reliability and safety of AI responses to laryngeal cancer queries.

PURPOSE: As online health information-seeking surges, concerns mount over the quality and safety of ...

The Efficacy of Machine Learning Models for Predicting the Prognosis of Heart Failure: A Systematic Review and Meta-Analysis.

INTRODUCTION: Heart failure (HF) is a major global public health concern. The application of machine...

Home-Based Cognitive Intervention for Healthy Older Adults Through Asking Robots Questions: Randomized Controlled Trial.

BACKGROUND: Asking questions is common in conversations, and while asking questions, we need to list...

Preventative treatment of tuberous sclerosis complex with sirolimus: Phase I safety and efficacy results.

OBJECTIVE: Tuberous sclerosis complex (TSC) results from overactivity of the mechanistic target of r...

Machine learning classification based on k-Nearest Neighbors for PolSAR data.

In this work, we focus on obtaining insights of the performances of some well-known machine learning...

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