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

You might also read

Related Articles

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

Sort by

Same author

Mitigating Financial Barriers to Trauma Care in Cameroon Through Locally Designed Quality Improvement.

World journal of surgery·2026

Same author

Discrepancies between self-reported alcohol use and breathalyzer results and factors associated with blood alcohol concentration positivity among road traffic injury patients in Cameroon.

Addiction (Abingdon, England)·2026

Same author

Data-adaptive identification of effect modifiers through stochastic shift interventions and cross-validated targeted learning.

Biostatistics (Oxford, England)·2026

Same author

Road traffic crash mortality in Southwest Cameroon: a multicenter hospital study.

African journal of emergency medicine : Revue africaine de la medecine d'urgence·2026

Same author

Long-term patterns of health services utilization after hospitalization for COPD. A nationwide registry-based study from Norway.

Respiratory medicine·2026

Same author

Uptake of visibility materials for injury prevention and associated factors among commercial motorcycle riders: a cross-sectional study in Cameroon.

BMC public health·2026

Search research articles

Related Experiment Video

Updated: Nov 22, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Right population, right resources, right algorithm: Using machine learning efficiently and effectively in surgical

Lauren Eyler Dang¹, Alan Hubbard², Fanny Nadia Dissak-Delon³

¹University of California, Berkeley, School of Public Health, Division of Biostatistics, Berkeley, CA; University of California, San Francisco, Department of Surgery, San Francisco, CA.

|January 8, 2021

Summary

Developing machine learning models for healthcare in low-resource settings requires careful consideration of the target population, available data, and appropriate algorithms. Open-source tools can help adapt these models globally for better surgical care and public health.

More Related Videos

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

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

Published on: August 16, 2020

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

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

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Nov 22, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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

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

Published on: August 16, 2020

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

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

Published on: July 22, 2025

Area of Science:

Medical Informatics
Public Health
Machine Learning

Background:

Machine learning (ML) shows promise for improving surgical care, diagnostics, and public health surveillance in low- and middle-income countries (LMICs).
Developing and implementing ML models in LMICs faces challenges due to limited data availability and resources.

Purpose of the Study:

To share practical lessons for developing context-appropriate and resource-conscious ML models for healthcare in LMICs.
To guide researchers in overcoming data limitations and optimizing algorithm development.

Main Methods:

Reviewing experiences and literature on ML model development in resource-limited settings.
Highlighting strategies for cohort selection, data integration, and algorithm choice (e.g., Super Learner ensemble).

Main Results:

Training cohorts should mirror the target population; recalibration aids model transportability.
Algorithms should leverage existing or easily obtainable data for seamless clinical integration.
The Super Learner algorithm can optimize model selection and minimize bias.

Conclusions:

Context-appropriate and resource-conscious ML models are achievable by considering population, resources, and algorithms.
Open-source code, apps, and training materials are crucial for adapting ML models globally.
Addressing data gaps and computational limitations will further advance ML in LMIC healthcare.