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:

You might also read

Related Articles

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

Sort by

Same author

A Comparison of Post-Traumatic Stress and Depressive Symptoms by Suicidal Ideation Among Black Transgender Women.

Research in nursing & health·2026

Same author

Exploring Inflation-Related Public Discourse Relevant to Social Determinants of Health Using Social Media Data.

International journal of environmental research and public health·2026

Same author

Interpretable machine learning for rare events: an inverse probability weighted application to understanding glucagon-like peptide-1 receptor agonists use and incident alcohol-related disorder.

Alcohol and alcoholism (Oxford, Oxfordshire)·2026

Same author

Application-Based Interventions for Family Caregivers of Older Adults: Scoping Review.

JMIR aging·2026

Same author

Segmentation and classification of hippocampal subregions using multi-task generative adversarial networks.

Scientific reports·2026

Same author

Deep learning multi-omics integration identifies new molecular subtypes of lung cancer.

BioData mining·2026

Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026

Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026

Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026

Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026

Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026

Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 11, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Early Sepsis Prediction Using Publicly Available Data: High-Performance AI/ML Models with First-Hour Clinical

Hao Wang¹, Destiny Pounds², Wenhui Zhang³

¹Department of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104, USA.

Diagnostics (Basel, Switzerland)

|November 13, 2025

Summary

This summary is machine-generated.

AI/ML models accurately predict sepsis using early clinical data. XGBoost models show strong potential for real-time sepsis screening, improving patient outcomes.

Keywords:

AI/ML algorithm early detection sepsis

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

Last Updated: Jan 11, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

Artificial Intelligence
Machine Learning
Medical Informatics

Background:

Early sepsis identification is crucial for reducing morbidity and mortality.
Delayed diagnosis significantly worsens patient outcomes.
AI/ML offers potential for improved early sepsis detection.

Purpose of the Study:

Develop and validate AI/ML models for early sepsis prediction.
Utilize structured EHR data, waveform data, and combined data sources.
Focus on maximizing recall for timely sepsis identification.

Main Methods:

Retrospective observational study using the CHoRUS dataset (AIM-AHEAD60).
Included adult patients with a final sepsis diagnosis.
Extracted first-hour EHR and waveform data; developed XGBoost, LightGBM, HistGB models.

Main Results:

XGBoost achieved the highest AUROC (0.922) with >80% recall.
Key predictors included lactate, leukocyte count, respiratory rate, and blood pressure trends.
Models performed robustly despite missing data.

Conclusions:

High-performing AI/ML models for early sepsis prediction are feasible using initial clinical data.
XGBoost models show significant potential for real-time clinical sepsis screening.
Publicly available datasets can support the development of effective sepsis prediction tools.