Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Inertia Tensor

Inertia Tensor

The concept of the inertia tensor is employed to depict the mass distribution and rotational inertia of a solid or rigid object. This tensor is expressed through a three-by-three matrix. Each component within this matrix corresponds to varying moments of inertia about specific axes.
The diagonal components of the inertia tensor matrix represent the moments of inertia concerning the principal axes of the object. These primary axes are defined as the axes where the object experiences the least...

Peptide Identification Using Tandem Mass Spectrometry

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...

Classification of Skeletal Muscle Fibers

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Seasonal Variation and Genetic Evaluation of Needle Catechin Content in Half-Sib Families of <i>Pinus taeda</i>.

Plants (Basel, Switzerland)·2026

Same author

Large Language Models for Summarizing Advance Care Planning Information From Goals of Care Notes in the EHR.

Learning health systems·2026

Same author

Realistic PET image synthesis from MRI for automated inference of brain atrophy and Alzheimer's.

iScience·2026

Same author

Transcriptome-Based Dissection of the Molecular Mechanisms Underlying Flooding Stress Responses of Eastern Cottonwood in the Floodplains of the Middle and Lower Reaches of the Yangtze River.

Plants (Basel, Switzerland)·2026

Same author

A Survey on Unifying Large Language Models and Knowledge Graphs for Biomedicine and Healthcare.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2026

Same author

Survivorship Navigator: Personalized Survivorship Care Plan Generation using Large Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026

Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026

Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026

Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026

Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026

Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 3, 2026

Quantification of Orofacial Phenotypes in Xenopus

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

Discriminative and Distinct Phenotyping by Constrained Tensor Factorization.

Yejin Kim¹, Robert El-Kareh², Jimeng Sun³

¹Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang, Korea.

Scientific Reports

|April 27, 2017

Summary

This summary is machine-generated.

We developed a new computational phenotyping method using supervised nonnegative tensor factorization on electronic health records (EHRs). This approach effectively identifies distinct patient phenotypes, outperforming existing ICU mortality prediction tools.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Related Experiment Videos

Last Updated: Mar 3, 2026

Quantification of Orofacial Phenotypes in Xenopus

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Area of Science:

Computational health informatics
Data science in healthcare
Clinical informatics

Background:

Electronic Health Record (EHR) systems generate vast amounts of complex healthcare data.
Extracting meaningful clinical concepts (phenotyping) from EHR data is challenging but crucial for clinical decision-making.

Purpose of the Study:

To introduce a novel supervised nonnegative tensor factorization (SNTF) methodology for computational phenotyping.
To derive discriminative and clinically interpretable patient phenotypes from EHR data.

Main Methods:

Represented the co-occurrence of diagnoses and prescriptions in EHRs as a third-order tensor.
Decomposed the tensor using the CP algorithm within a supervised SNTF framework.
Evaluated model performance using the MIMIC-III Intensive Care Unit (ICU) database.

Main Results:

The proposed SNTF method successfully derived distinct and clinically relevant phenotypes.
The derived phenotypes demonstrated superior discriminative power compared to established ICU mortality calculators (APACHE II, SAPS II).
Identified phenotypes included sepsis with acute kidney injury, cardiac surgery complications, anemia, respiratory failure, and end-stage dementia.

Conclusions:

Supervised nonnegative tensor factorization is a powerful technique for computational phenotyping from EHR data.
This methodology offers a promising approach to uncover complex patient subgroups and improve clinical risk stratification.
The derived phenotypes provide valuable insights for healthcare providers and researchers.