AI Medical Compendium Topic:
Cluster Analysis

Clear Filters Showing 901 to 910 of 1337 articles

Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method.

Scientific reports
Understanding genetic mechanism of complex diseases is a serious challenge. Existing methods often neglect the heterogeneity phenomenon of complex diseases, resulting in lack of power or low reproducibility. Addressing heterogeneity when detecting ep...

Identification of Clinically Meaningful Plasma Transfusion Subgroups Using Unsupervised Random Forest Clustering.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Statistical techniques such as propensity score matching and instrumental variable are commonly employed to "simulate" randomization and adjust for measured confounders in comparative effectiveness research. Despite such adjustments, the results of t...

Exploiting Unlabeled Texts with Clustering-based Instance Selection for Medical Relation Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Classifying relations between pairs of medical concepts in clinical texts is a crucial task to acquire empirical evidence relevant to patient care. Due to limited labeled data and extremely unbalanced class distributions, medical relation classificat...

Visual Kinship Recognition of Families in the Wild.

IEEE transactions on pattern analysis and machine intelligence
We present the largest database for visual kinship recognition, Families In the Wild (FIW), with over 13,000 family photos of 1,000 family trees with 4-to-38 members. It took only a small team to build FIW with efficient labeling tools and work-flow....

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach.

PloS one
Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screen...

Design of double fuzzy clustering-driven context neural networks.

Neural networks : the official journal of the International Neural Network Society
In this study, we introduce a novel category of double fuzzy clustering-driven context neural networks (DFCCNNs). The study is focused on the development of advanced design methodologies for redesigning the structure of conventional fuzzy clustering-...

HMMER Cut-off Threshold Tool (HMMERCTTER): Supervised classification of superfamily protein sequences with a reliable cut-off threshold.

PloS one
BACKGROUND: Protein superfamilies can be divided into subfamilies of proteins with different functional characteristics. Their sequences can be classified hierarchically, which is part of sequence function assignation. Typically, there are no clear s...

Stroke localization and classification using microwave tomography with k-means clustering and support vector machine.

Bioelectromagnetics
For any chance for stroke patients to survive, the stroke type should be classified to enable giving medication within a few hours of the onset of symptoms. In this paper, a microwave-based stroke localization and classification framework is proposed...

Determining Anxiety in Obsessive Compulsive Disorder through Behavioural Clustering and Variations in Repetition Intensity.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Over the last decade, the application of computer vision techniques to the analysis of behavioural patterns has seen a considerable increase in research interest. One such interesting and recent application is the visual be...