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Enhancing post-training evaluation of annual performance agreement training: A fusion of fsQCA and artificial neural network approach.

This study aims to enhance the post-training evaluation of the annual performance agreement (APA) tr...

Frontiers of machine learning in smart food safety.

Integration of machine learning (ML) technologies into the realm of smart food safety represents a r...

Legal and Ethical Considerations of Artificial Intelligence for Residents in Post-Acute and Long-Term Care.

This article proposes a framework for examining the ethical and legal concerns for using artificial ...

Persistent spiking activity in neuromorphic circuits incorporating post-inhibitory rebound excitation.

. This study introduces a novel approach for integrating the post-inhibitory rebound excitation (PIR...

Prediction of post-delivery hemoglobin levels with machine learning algorithms.

Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, en...

Development and validation of an automatic machine learning model to predict abnormal increase of transaminase in valproic acid-treated epilepsy.

Valproic acid (VPA) is a primary medication for epilepsy, yet its hepatotoxicity consistently raises...

Falls Prevention Using AI and Remote Surveillance in Nursing Homes.

The older population of United States is growing, with more adults having complicated medical condit...

A real-world pharmacovigilance study on cardiovascular adverse events of tisagenlecleucel using machine learning approach.

Chimeric antigen receptor T-cell (CAR-T) therapies are a paradigm-shifting therapeutic in patients w...

Post-stroke hand gesture recognition via one-shot transfer learning using prototypical networks.

BACKGROUND: In-home rehabilitation systems are a promising, potential alternative to conventional th...

Quantifying social capital creation in post-disaster recovery aid in Indonesia: methodological innovation by an AI-based language model.

Smooth interaction with a disaster-affected community can create and strengthen its social capital, ...

Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification.

OBJECTIVE: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the l...

Advances in artificial intelligence for drug delivery and development: A comprehensive review.

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, ...

Cancer diagnosis in the post-coronavirus disease era: the promising role of telepathology and artificial intelligence.

Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration.

Adverse drug reactions are a common cause of morbidity in health care. The US Food and Drug Administ...

Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records.

BACKGROUND: Extracting research of domain criteria (RDoC) from high-risk populations like those with...

Explainable hypoglycemia prediction models through dynamic structured grammatical evolution.

Effective blood glucose management is crucial for people with diabetes to avoid acute complications....

Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data.

INTRODUCTION: Intracerebral hemorrhage represents 15 % of all strokes and it is associated with a hi...

Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning.

Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a pot...

Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.

PURPOSE: High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervo...

Impact Exploration of Spatiotemporal Feature Derivation and Selection on Machine Learning-Based Predictive Models for Post-Embolization Cerebral Aneurysm Recanalization.

PURPOSE: To enhance the performance of machine learning (ML) models for the post-embolization recana...

Mitigating Trunk Compensatory Movements in Post-Stroke Survivors through Visual Feedback during Robotic-Assisted Arm Reaching Exercises.

Trunk compensatory movements frequently manifest during robotic-assisted arm reaching exercises for ...