Menopause

Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning.

The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NE...

Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence.

Maps of disease burden are a core tool needed for the control and elimination of malaria. Reliable r...

Comparison study of classification methods of intramuscular electromyography data for non-human primate model of traumatic spinal cord injury.

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of s...

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm.

Non-small-cell lung cancer (NSCLC) patients often develop bone metastases (BM), and the overall surv...

What is the optimal surveillance strategy for non-dysplastic Barrett's esophagus?

PURPOSE OF REVIEW: There is conflicting data on the effectiveness of the currently recommended endos...

Pinning bipartite synchronization for inertial coupled delayed neural networks with signed digraph via non-reduced order method.

The study investigates bipartite synchronization of inertial coupled delayed neural networks (ICDNNs...

Effects of non-facilitated meaningful activities for people with dementia in long-term care facilities: A systematic review.

This systematic review sought to evaluate the effectiveness of non-facilitated meaningful activities...

Ab initio GO-based mining for non-tandem-duplicated functional clusters in three model plant diploid genomes.

A functional Non-Tandem Duplicated Cluster (FNTDC) is a group of non-tandem-duplicated genes that ar...

Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa.

Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glauc...

Integrative blockwise sparse analysis for tissue characterization and classification.

The topic of sparse representation of samples in high dimensional spaces has attracted growing inter...

Development and Validation of a Deep Learning Model for Non-Small Cell Lung Cancer Survival.

IMPORTANCE: There is a lack of studies exploring the performance of a deep learning survival neural ...

Prognosis and Treatment of Non-Small Cell Lung Cancer in the Age of Deep Learning.

Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning.

Precision oncology involves effectively selecting drugs for cancer patients and planning an effectiv...

Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings.

High resolution cervical auscultation is a very promising noninvasive method for dysphagia screening...

Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils.

In the last decade essential oils have attracted scientists with a constant increase rate of more th...

Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma.

Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies...

Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning.

Pressure injuries represent a major concern in many nations. These wounds result from prolonged pres...

Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score.

Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. Ho...

Non - invasive modelling methodology for the diagnosis of coronary artery disease using fuzzy cognitive maps.

Cardiovascular diseases (CVD) and strokes produce immense health and economic burdens globally. Coro...

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.

Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to s...

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach.

BACKGROUND: Previous attempts to identify predictors of treatment outcomes in body dysmorphic disord...