The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal validation in a retrospective cohort

Existing resampling techniques had a variable impact on models, depending on the algorithms and the evaluation metrics. Future research is needed to improve predictive performances in the setting of severe class imbalance.

Authors

Janny Xue Chen Ke (ORCID: https://orcid.org/0000-0002-3121-9780)
Paula Branco (ORCID: https://orcid.org/0000-0002-9917-3694)
Ronald B. George (ORCID: https://orcid.org/0000-0002-1848-036X)
Arunachalam DhakshinaMurthy

Institutions

Mount Sinai Hospital (CA)
Dalhousie University (CA)
Providence Health Care (CA)
University of British Columbia (CA)
University of Ottawa (CA)
University of Toronto (CA)
St. Paul's Hospital (CA)
Carleton University (CA)

Publication Details

Journal: Discover Artificial Intelligence
Published: 2024-11-26
DOI: https://doi.org/10.1007/s44163-024-00199-0
Citations: 5
Primary Topic: Imbalanced Data Classification Techniques
Type: article
Field-Weighted Citation Impact: 1.74

Funders

Providence Health Care
Dalhousie University
Natural Sciences and Engineering Research Council of Canada

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal validation in a retrospective cohort

Janny Xue Chen Ke, Paula Branco, Ronald B. George, Arunachalam DhakshinaMurthy

Nov 26, 2024

5 citations

Discover Artificial Intelligence

Imbalanced Data Classification Techniques

1.74

article

The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal validation in a retrospective cohort

Janny Xue Chen Ke, Paula Branco, Ronald B. George, Arunachalam DhakshinaMurthy

November 26, 2024article en

5 citations

Abstract

Existing resampling techniques had a variable impact on models, depending on the algorithms and the evaluation metrics. Future research is needed to improve predictive performances in the setting of severe class imbalance.

Discover Artificial IntelligenceVol. 4(1)

Mount Sinai Hospital (CA), Dalhousie University (CA), Providence Health Care (CA), University of British Columbia (CA), University of Ottawa (CA), University of Toronto (CA), St. Paul's Hospital (CA), Carleton University (CA)

Providence Health Care, Dalhousie University, Natural Sciences and Engineering Research Council of Canada

Openalex Percentile: Top 13%

Imbalanced Data Classification Techniques

1.74

DOI Open Access PDF OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal validation in a retrospective cohort — Janny Xue Chen Ke, Paula Branco, et al. · Discover Artificial Intelligence (2024) | TGRS Research Map | TGRS