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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...

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Home
Ai-driven Reclassification Of Multiple Sclerosis Progression.

Home
Ai-driven Reclassification Of Multiple Sclerosis Progression.

Related Experiment Video

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

Published on: June 30, 2014

AI-driven reclassification of multiple sclerosis progression.

Habib Ganjgahi^1,2, Dieter A Häring³, Piet Aarden³

¹Department of Statistics, University of Oxford, Oxford, UK.

Nature Medicine

|August 20, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Multiple sclerosis (MS) is reclassified using machine learning into a disease continuum, not distinct subtypes. This new model, based on disability, brain damage, and activity, improves understanding and treatment for millions affected by MS.

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Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Published on: February 19, 2021

Area of Science:

Neurology
Data Science
Medical Classification

Background:

Traditional multiple sclerosis (MS) subtypes lack prognostic value and hinder drug discovery.
Current classification fails to capture MS pathobiology and disease evolution.
A new approach is needed to better understand and manage MS.

Purpose of the Study:

To develop a data-driven classification of MS disease evolution.
To identify key dimensions defining MS disease states.
To propose a refined classification for improved patient management and drug discovery.

Main Methods:

Analysis of a large clinical trial database (approx. 8,000 patients) using probabilistic machine learning.
Inclusion of clinical data, MRI scans, and patient visits.

Validation in an independent clinical trial database and real-world cohort (over 4,000 patients).

Main Results:

Identified four dimensions of MS: physical disability, brain damage, relapse, and subclinical disease activity.
Defined two poles of a disease spectrum: Early/Mild/Evolving (EME) MS and advanced MS.
Demonstrated transitions to advanced MS via brain damage accumulation, with progression independent of relapses.

Conclusions:

MS should be viewed as a disease continuum rather than distinct subtypes.
The proposed classification offers a unifying understanding of MS.
This approach can enhance patient management and accelerate drug discovery for MS.