Staffing & Scheduling

Machine Learning in Small-Molecule Mass Spectrometry.

Tandem mass spectrometry (MS/MS) is crucial for small-molecule analysis; however, traditional computational methods are limited by incomplete reference libraries and complex data processing. Machine learning (ML) is transforming small-molecule mass spectrometry in three key directions: () predicting MS/MS spectra and related physicochemical properties to expand reference libraries, () improving sp...

Deeply supervised two stage generative adversarial network for stain normalization.

The color variations present in histopathological images pose a significant challenge to computational pathology and, consequently, negatively affect the performance of certain pathological image analysis methods, especially those based on deep learning techniques. To date, several methods have been proposed to mitigate this issue. However, these methods either produce images with low texture rete...

Is it necessary? A framework for assessing the utility of A.I. in HRM practices.

Artificial Intelligence is expected to be a value-adding intervention in HRM processes; however, there is still a large gap between its perception of ...

Integration of Virtual Technology and Artificial Intelligence Improves Satisfaction, Patient Safety, and Nursing Workforce Efficiency.

BACKGROUND: Virtual care technology including artificial intelligence (AI) may augment nursing functions creating flexibility in staffing that reduces...

Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study.

This study evaluates the performance of deep learning models in the prediction of the end time of procedures performed in the cardiac catheterization ...

Artificial Intelligence in Peer Review: Enhancing Efficiency While Preserving Integrity.

The rapid advancement of artificial intelligence (AI) has transformed various aspects of scientific research, including academic publishing and peer r...

Learning from leading indicators to predict long-term dynamics of hourly electricity generation from multiple resources.

Electricity is generated through various resources and then flows between regions via a complex system (grid). Imbalances in electricity generation ca...

The development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas.

Linear and volumetric analysis are the typical methods to measure tumor size. 3D volumetric analysis has risen in popularity; however, this is very ti...

DeepSeek in Healthcare: Revealing Opportunities and Steering Challenges of a New Open-Source Artificial Intelligence Frontier.

Generative Artificial Intelligence (GAI) has driven several advancements in healthcare, with large language models (LLMs) such as OpenAI's ChatGPT, Go...

PFPRNet: A Phase-Wise Feature Pyramid With Retention Network for Polyp Segmentation.

Early detection of colonic polyps is crucial for the prevention and diagnosis of colorectal cancer. Currently, deep learning-based polyp segmentation ...

Memory-Dependent Computation and Learning in Spiking Neural Networks Through Hebbian Plasticity.

Spiking neural networks (SNNs) are the basis for many energy-efficient neuromorphic hardware systems. While there has been substantial progress in SNN...

Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes.

Citrullination is a critical yet understudied post-translational modification (PTM) implicated in various biological processes. Exploring its role in ...

Toward Machine Learning Electrospray Ionization Sensitivity Prediction for Semiquantitative Lipidomics in Stem Cells.

Specificity, sensitivity, and high metabolite coverage make mass spectrometry (MS) one of the most valuable tools in metabolomics and lipidomics. Howe...

Improved workflow for constructing machine learning models: Predicting retention times and peak widths in oligonucleotide separation.

This study presents an improved workflow to support the development of machine learning models to predict oligonucleotide retention times, peak widths...

Predicting abatacept retention using machine learning.

BACKGROUND: The incorporation of machine learning is becoming more prevalent in the clinical setting. By predicting clinical outcomes, machine learnin...

NLP for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review.

This review examines the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and cli...

Multimodal Cross Global Learnable Attention Network for MR images denoising with arbitrary modal missing.

Magnetic Resonance Imaging (MRI) generates medical images of multiple sequences, i.e., multimodal, from different contrasts. However, noise will reduc...

QuanFormer: A Transformer-Based Precise Peak Detection and Quantification Tool in LC-MS-Based Metabolomics.

In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. C...

Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach.

The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate bot...

Predicting biomass conversion and COD removal in wastewater treatment by phototrophic bacteria with interpretable machine learning.

Photosynthetic bacteria (PSB) excel in wastewater treatment by removing pollutants and generating biomass but are challenging to optimize due to compl...