Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
The promise behind many advanced digital technologies in healthcare is to provide novel and accurate information, aiding medical experts to navigate and, ultimately, decrease uncertainty in their clinical work. However, sociological studies have started to show that these technologies are not producing straightforward objective knowledge, but instead often become associated with new uncertainties ...
The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of cardiac conditions. The advancement of deep learning has led to the emergence of automated multi-lead ECG diagnostic networks, which have become essential in the fields of biomedical engineering and clinical cardiac disease diagnosis. Intelligent ECG diagnosis techniques encompass Recurrent Neural Net...
BACKGROUND: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning...
Extensive studies on improving anammox performance have taken place for decades with particular focuses on its operational and environmental factors, ...
BACKGROUND: Participant recruitment in rural and hard-to-reach (HTR) populations can present unique challenges. These challenges are further exacerbat...
Proteome analysis currently heavily relies on tandem mass spectrometry (MS/MS), which does not fully utilize MS1 features, as many precursors remain u...
In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling...
Automatic eligibility criteria parsing in clinical trials is crucial for cohort recruitment leading to data validity and trial completion. Recent year...
In this study, four machine learning (ML) prediction models were developed to predict and optimize the production performance of caproic acid based on...
Over the past decades, air pollution has caused severe environmental and public health problems. According to the World Health Organization (WHO), fin...
Pre-training deep learning models with large data sets of natural images, such as ImageNet, has become the standard for endoscopic image analysis. Thi...
Domain Generalization (DG) focuses on the Out-Of-Distribution (OOD) generalization, which is able to learn a robust model that generalizes the knowled...
BACKGROUND: In Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor ass...
The recently introduced neural operator (NO) has been employed as a gain approximator in the backstepping stabilization control of first-order hyperbo...
In recent decades, the rapid advances in information technology have promoted a widespread deployment of medical cyber-physical systems (MCPS), especi...
AI is now an integral part of everyday decision-making, assisting us in both routine and high-stakes choices. These AI models often learn from human b...
Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumo...
Many antimicrobial peptides directly disrupt bacterial membranes yet can also damage mammalian membranes. It is therefore central to their therapeutic...
BACKGROUND: Clinical decision-making in oncology is a complex process influenced by numerous disease-related factors, patient demographics, and logist...
Large language models (LLMs), a natural language processing technology based on deep learning, are currently in the spotlight. These models closely mi...