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

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

You might also read

Related Articles

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

Sort by

Same author

Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies.

Environmental monitoring and assessment·2024

Same author

SCBC: Smart city monitoring with blockchain using Internet of Things for and neuro fuzzy procedures.

Mathematical biosciences and engineering : MBE·2023

Same author

A comparative recognition research on excretory organism in medical applications using artificial neural networks.

Frontiers in bioengineering and biotechnology·2023

Same author

Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease.

Biomedicines·2023

Same author

A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm.

Diagnostics (Basel, Switzerland)·2022

Same author

Tourism cloud management system: the impact of smart tourism.

Journal of cloud computing (Heidelberg, Germany)·2022

Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026

Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026

Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026

Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026

Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026

Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Enhancing disease clustering through symptom-based analysis and large language model interpretations.

Efe Onojete¹, Ebuka Ibeke², Chinedu Pascal Ezenkwu¹

¹School of Computing, Engineering and Technology, Robert Gordon University, Garthdee Road, Garthdee, Aberdeen, AB10 7AQ, United Kingdom.

Scientific Reports

|October 21, 2025

Summary

This summary is machine-generated.

This study used machine learning for disease classification based on symptoms. Large Language Models (LLMs) like GPT-4o improved understanding of disease clusters, aiding healthcare professionals.

Keywords:

Clustering Diseases Interpretability Large language model Machine learning Symptoms Unsupervised learning

More Related Videos

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: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Related Experiment Videos

Last Updated: Jan 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Area of Science:

Medical Informatics
Computational Biology
Artificial Intelligence in Healthcare

Background:

Diseases are often linked to environmental factors and lifestyle, presenting with overlapping symptoms.
Identifying symptom-based disease relationships is crucial for effective outbreak response and treatment planning.

Purpose of the Study:

To enhance disease classification using unsupervised machine learning and symptom-based cluster analysis.
To leverage Large Language Models (LLMs) for interpreting complex machine learning outputs in a healthcare context.

Main Methods:

Applied unsupervised machine learning algorithms to diverse symptom datasets for cluster analysis.
Integrated OpenAI's Generative Pretrained Transformer (GPT), specifically GPT-4o, for interpreting and communicating findings.
Analyzed patterns and relationships within symptom data to identify disease subtypes and associations.

Main Results:

Achieved significant improvement in defining distinct disease clusters based on symptom relationships.
Demonstrated GPT-4o's efficacy in simplifying complex machine learning insights for healthcare professionals.
Uncovered novel associations and subtypes in disease manifestation through cluster analysis.

Conclusions:

Machine learning, particularly symptom-based clustering, enhances disease classification accuracy.
LLMs like GPT-4o are valuable tools for bridging the gap between AI-driven insights and clinical understanding.
The findings provide deeper insights into disease characteristics and clustering for improved healthcare strategies.