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Cluster Analysis

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Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia.

Scientific reports
Severe pneumonia results in high morbidity and mortality despite advanced treatments. This study investigates thoracic muscle mass from chest CT scans as a biomarker for predicting clinical outcomes in ICU patients with severe pneumonia. Analyzing el...

Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information.

Neural networks : the official journal of the International Neural Network Society
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. Most existing DCMVC algorithms focus on exploring the consistency infor...

Computational Insights into Reproductive Toxicity: Clustering, Mechanism Analysis, and Predictive Models.

International journal of molecular sciences
Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identif...

FSRW: fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare.

Scientific reports
The Internet of Things (IoT) is an extensive system of interrelated devices equipped with sensors to monitor and track real world objects, spanning several verticals, covering many different industries. The IoT's promise is capturing interest as its ...

Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Individuals with a Fontan circulation encompass a heterogeneous group with adverse outcomes linked to ventricular dilation, dysfunction, and dyssynchrony. The purpose of this study was to assess if unsupervised machine learning cluster an...

T-distributed Stochastic Neighbor Network for unsupervised representation learning.

Neural networks : the official journal of the International Neural Network Society
Unsupervised representation learning (URL) is still lack of a reasonable operator (e.g. convolution kernel) for exploring meaningful structural information from generic data including vector, image and tabular data. In this paper, we propose a simple...

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.

International journal of neural systems
Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from hi...

Machine learning-based cluster analysis identifies four unique phenotypes of patients with degenerative cervical myelopathy with distinct clinical profiles and long-term functional and neurological outcomes.

EBioMedicine
BACKGROUND: Degenerative cervical myelopathy (DCM), the predominant cause of spinal cord dysfunction among adults, exhibits diverse interrelated symptoms and significant heterogeneity in clinical presentation. This study sought to use machine learnin...

Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis.

Scientific reports
Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from insufficient insulin secretion or reduced insulin activity. Epidemiological survey results show that ab...

Progressive Neighbor-masked Contrastive Learning for Fusion-style Deep Multi-view Clustering.

Neural networks : the official journal of the International Neural Network Society
Fusion-style Deep Multi-view Clustering (FDMC) can efficiently integrate comprehensive feature information from latent embeddings of multiple views and has drawn much attention recently. However, existing FDMC methods suffer from the interference of ...