Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

135
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:
135

You might also read

Related Articles

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

Sort by
Same author

Tuberculous tenosynovitis and bursitis: imaging findings in 21 cases.

Radiology·1996
Same author

Recombinant mitotoxin basic fibroblast growth factor-saporin reduces venous anastomotic intimal hyperplasia in the arteriovenous graft.

Circulation·1996
Same author

Measurement of urinary estrogen metabolites using a monoclonal enzyme-linked immunoassay kit: assay performance and feasibility for epidemiological studies.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·1996
Same author

Differences in cholinergic responses from outer hair cells of rat and guinea pig.

Hearing research·1996
Same author

Pharmacokinetics of retinoids in women after meal consumption or vitamin A supplementation.

Journal of clinical pharmacology·1996
Same author

Determination of the amino acid residue involved in [3H]beta-funaltrexamine covalent binding in the cloned rat mu-opioid receptor.

The Journal of biological chemistry·1996

Related Experiment Video

Updated: Jul 12, 2025

Microscopy-based Assays for High-throughput Screening of Host Factors Involved in Brucella Infection of Hela Cells
15:29

Microscopy-based Assays for High-throughput Screening of Host Factors Involved in Brucella Infection of Hela Cells

Published on: August 5, 2016

8.3K

[Development of auxiliary early predicting model for human brucellosis using machine learning algorithm].

W Wang1, R Zhou2, C Chen3

  • 1Department of Blood Transfusion, Beijing Ditan Hospital, Capital Medical University,Beijing 100015,China.

Zhonghua Yu Fang Yi Xue Za Zhi [Chinese Journal of Preventive Medicine]
|October 20, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts brucellosis using clinical data. Support vector machines achieved 95.9% recall, enabling earlier diagnosis and treatment of this infectious disease.

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Related Experiment Videos

Last Updated: Jul 12, 2025

Microscopy-based Assays for High-throughput Screening of Host Factors Involved in Brucella Infection of Hela Cells
15:29

Microscopy-based Assays for High-throughput Screening of Host Factors Involved in Brucella Infection of Hela Cells

Published on: August 5, 2016

8.3K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Area of Science:

  • * Infectious Disease Epidemiology
  • * Computational Biology
  • * Clinical Diagnostics

Context:

  • * Brucellosis diagnosis relies on clinical symptoms and lab tests, often leading to delays.
  • * Machine learning offers potential for improving diagnostic efficiency and early detection.
  • * A case-control study retrospectively analyzed clinical and blood count data from brucellosis patients and healthy individuals.

Purpose:

  • * To develop and evaluate machine learning models for early brucellosis prediction.
  • * To identify key clinical and hematological features indicative of brucellosis.
  • * To enhance the efficiency and accuracy of brucellosis diagnosis.

Summary:

  • * Five machine learning algorithms (random forest, Naive Bayes, decision tree, logistic regression, support vector machine) were employed to build an early brucellosis prediction model.
  • * The support vector machine model demonstrated superior predictive performance, achieving an Area Under the Curve (AUC) of 0.991.
  • * Key predictors identified include platelet distribution width (PDW), basophil relative value (BASO%), red blood cell distribution width coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV).

Impact:

  • * The developed machine learning model provides a high-precision method for early brucellosis detection.
  • * Early identification can significantly improve patient outcomes through timely treatment initiation.
  • * This approach has the potential to reduce diagnostic delays and optimize resource allocation in brucellosis management.