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

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

You might also read

Related Articles

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

Sort by

Same author

Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory.

Medical image analysis·2026

Same author

SA-RAG: Structured and adaptive retrieval-augmented generation for multi-hop question answering.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Self-supervised exceptional prototypical network for few-shot grading of gastric intestinal metaplasia.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

From motivation to meaning in medical humanities: a mixed-methods study of Chinese medical students' value internalization.

BMC medical education·2026

Same author

Toward Effective Model Merging in Semantic Segmentation.

IEEE transactions on neural networks and learning systems·2025

Same author

Finer-Grained Dynamic Functional Graph Structure Learning for EEG Sequence Modeling.

IEEE journal of biomedical and health informatics·2025

Same journal

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks for In-Home Activity Learning of Mild Cognitive Impairment.

IEEE journal of biomedical and health informatics·2026

Same journal

Integrating Multi-View Residue Graph and Protein Language Model for Cell-Penetrating Peptide Prediction via Global-Local Graph Aggregation and Cross-Attentive Fusion.

IEEE journal of biomedical and health informatics·2026

Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026

Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026

Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026

Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 13, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Multi-Modal Disease Prediction With Hierarchical Self-Supervised Learning.

Zhe Qu, Taihua Chen, Xin Zhou

IEEE Journal of Biomedical and Health Informatics

|April 16, 2025

Summary

This summary is machine-generated.

This study introduces HierSSL, a new framework for multi-modal disease prediction using hierarchical self-supervised learning. HierSSL effectively integrates diverse healthcare data, improving prediction accuracy and model robustness.

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: May 13, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Computational biology
Medical informatics
Machine learning for healthcare

Background:

Healthcare data proliferation offers opportunities for disease prediction.
Multi-modal data (imaging, biochemical, clinical records) aids diagnostic model development.
Graph Neural Networks (GNNs) model patient relationships but struggle with noisy data and restrictive constraints.

Purpose of the Study:

To propose HierSSL, a novel multi-modal disease prediction framework.
To enhance representational learning using dual-scale self-supervision (local and global).
To improve GNN robustness and handle noisy, low-quality multi-modal data.

Main Methods:

Hierarchical Self-Supervised Learning (HierSSL) framework.
Dual-scale self-supervision (local inter-modality dependencies and global community patterns).
Integration of feature consistency constraints and graph contrastive learning for multi-modal feature optimization.

Main Results:

HierSSL demonstrated statistically significant performance improvements on two disease prediction datasets.
The framework effectively captures local and global patterns in multi-modal data.
HierSSL shows enhanced robustness in handling noisy and low-quality data.

Conclusions:

HierSSL offers a robust approach to multi-modal disease prediction.
The dual-scale self-supervision effectively integrates diverse healthcare data.
This method advances the application of GNNs in clinical predictive modeling.