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

You might also read

Related Articles

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

Sort by
Same author

BargCrEx: A System for Bargaining Based Aggregation of Crowd and Expert Opinions in Crowdsourcing.

Group decision and negotiation·2022
Same author

Performance comparison of nonlinear and linear regression algorithms coupled with different attribute selection methods for quantitative structure - retention relationships modelling in micellar liquid chromatography.

Journal of chromatography. A·2020
Same author

Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2019
Same author

IDPpi: Protein-Protein Interaction Analyses of Human Intrinsically Disordered Proteins.

Scientific reports·2018
Same author

Secondary analysis of electronic health records in critical care medicine.

Annals of translational medicine·2018
Same author

Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

Artificial intelligence in medicine·2016

Related Experiment Video

Updated: Apr 28, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.1K

Cloud based metalearning system for predictive modeling of biomedical data.

Milan Vukićević1, Sandro Radovanović1, Miloš Milovanović1

  • 1Faculty of Organizational Sciences, University of Belgrade, Jove Ilića 154, 11000 Belgrade, Serbia.

Thescientificworldjournal
|June 4, 2014
PubMed
Summary
This summary is machine-generated.

Analyzing large biomedical datasets for healthcare improvement is challenging. This study proposes a cloud system using meta-learning and big data technologies to efficiently select and apply predictive models for better healthcare outcomes.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.2K

Related Experiment Videos

Last Updated: Apr 28, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.1K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.2K

Area of Science:

  • Biomedical Informatics
  • Data Science
  • Computational Biology

Background:

  • The exponential growth of biomedical data presents significant opportunities for predictive modeling in healthcare.
  • Analyzing large-scale biomedical datasets is computationally intensive and challenging for traditional data mining algorithms.

Purpose of the Study:

  • To address the computational challenges in analyzing large biomedical datasets.
  • To propose an integrated cloud-based system for efficient biomedical data analysis and predictive modeling.

Main Methods:

  • Implementation of a cloud-based system leveraging open-source big data technologies.
  • Integration of a meta-learning framework for algorithm selection and ranking.
  • Application of the system to analyze complex biomedical data.

Main Results:

  • Demonstrated efficient processing of large-scale biomedical data.
  • Successfully identified and ranked optimal predictive algorithms for specific datasets.
  • Facilitated improved accuracy and efficiency in healthcare predictive modeling.

Conclusions:

  • The proposed cloud-based system effectively overcomes computational hurdles in biomedical data analysis.
  • Meta-learning enhances the selection of appropriate predictive models, leading to better healthcare insights.
  • This approach supports the advancement of data-driven healthcare solutions.