State Required CME

Identifying and Reporting Child abuse

Latest AI and machine learning research in identifying and reporting child abuse for healthcare professionals.

2,668 articles
Stay Ahead - Weekly Identifying and Reporting Child abuse research updates
Subscribe
Browse Specialties
Showing 169-189 of 2,668 articles
TAU-DI Net: A Multi-Scale Convolutional Network Combining Prob-Sparse Attention for EEG-based Depression Identification.

EEG-based detection of major depression disorder (MDD) plays a pivotal role in the subsequent treatm...

GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction.

Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent...

Approximating Nonlinear Functions With Latent Boundaries in Low-Rank Excitatory-Inhibitory Spiking Networks.

Deep feedforward and recurrent neural networks have become successful functional models of the brain...

Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems.

In the drug discovery process, time and costs are the most typical problems resulting from the exper...

Automatic recognition of white blood cell images with memory efficient superpixel metric GNN: SMGNN.

An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of ma...

MEG-PPIS: a fast protein-protein interaction site prediction method based on multi-scale graph information and equivariant graph neural network.

MOTIVATION: Protein-protein interaction sites (PPIS) are crucial for deciphering protein action mech...

Predicting future falls in older people using natural language processing of general practitioners' clinical notes.

BACKGROUND: Falls in older people are common and morbid. Prediction models can help identifying indi...

Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states.

Modern semiempirical electronic structure methods have considerable promise in drug discovery as uni...

DeepHisCoM: deep learning pathway analysis using hierarchical structural component models.

Many statistical methods for pathway analysis have been used to identify pathways associated with th...

MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction.

MOTIVATION: During lead compound optimization, it is crucial to identify pathways where a drug-like ...

Deep Learning and Explainable Artificial Intelligence to Predict Patients' Choice of Hospital Levels in Urban and Rural Areas.

Maldistribution of healthcare resources among urban and rural areas is a significant challenge world...

Promoting the Importance of Recall Visits Among Dental Patients in India Using a Semi-Autonomous AI System.

In many developing countries like India, there is a widespread lack of general awareness about the i...

BridgeDPI: a novel Graph Neural Network for predicting drug-protein interactions.

MOTIVATION: Exploring drug-protein interactions (DPIs) provides a rapid and precise approach to assi...

Gell-Mann-Low Criticality in Neural Networks.

Criticality is deeply related to optimal computational capacity. The lack of a renormalized theory o...

Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a convolutional neural network.

Since 2015, a fast growing number of deep learning-based methods have been proposed for protein-liga...

Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Although ge...

Enriching contextualized language model from knowledge graph for biomedical information extraction.

Biomedical information extraction (BioIE) is an important task. The aim is to analyze biomedical tex...

On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

A review, recently published in this journal by Fang (2019), showed that methods trained for the pre...

Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network.

MOTIVATION: Interactions among cis-regulatory elements such as enhancers and promoters are main driv...

Fused Group Lasso Regularized Multi-Task Feature Learning and Its Application to the Cognitive Performance Prediction of Alzheimer's Disease.

Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, e...

Browse Specialties