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

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

6.2K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
6.2K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.6K
3.6K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.1K
Documentation in Long-Term and Home Healthcare Setting01:29

Documentation in Long-Term and Home Healthcare Setting

1.4K
Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
Long-Term Care Facilities
1.4K

You might also read

Related Articles

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

Sort by
Same author

Photo-Induced Synthesis of Bioplastics from Xylan.

Research (Washington, D.C.)·2026
Same author

Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.

Journal of clinical and translational science·2026
Same author

Attention guided fair artificial intelligence modeling for skin cancer diagnosis.

NPJ digital medicine·2026
Same author

Strategies for mitigating artificial intelligence bias in healthcare: a systematic review.

JAMIA open·2026
Same author

Defining Prenatal Care Surveillance Metrics Using Electronic Health Record Data.

JAMA health forum·2026
Same author

Cardiovascular Disease Risk and Noncardiovascular Chronic Disease Burden by Housing Status.

Journal of the American Heart Association·2026

Related Experiment Video

Updated: Jan 18, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.6K

Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.

Siqi Li1, Xin Li1, Kunyu Yu1

  • 1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.

Health Data Science
|September 8, 2025
PubMed
Summary

Transfer learning (TL) can improve medical research models using existing data. However, its use with structured clinical data is limited, especially concerning privacy and multi-site studies.

More Related Videos

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.4K

Related Experiment Videos

Last Updated: Jan 18, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.6K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.4K

Area of Science:

  • Machine Learning in Healthcare
  • Biomedical Data Science
  • Clinical Research Methodologies

Background:

  • Low-resource settings face data quantity/quality challenges for robust clinical and biomedical models.
  • Transfer learning (TL) offers a solution by leveraging pretrained models to enhance new model performance.
  • Despite its potential, TL applications in medical research, particularly with structured data, remain limited.

Purpose of the Study:

  • To analyze current applications of transfer learning in medical research using structured clinical and biomedical data.
  • To identify overlooked TL techniques and propose improvements for future healthcare research.
  • To address challenges in applying TL, including data limitations, privacy, and regional disparities.

Main Methods:

  • Conducted a systematic literature review following PRISMA-ScR guidelines.
  • Searched major databases (SCOPUS, MEDLINE, Web of Science, Embase, CINAHL) for relevant studies.
  • Included articles employing TL with structured clinical or biomedical data.

Main Results:

  • Screened 5,080 papers, with 86 meeting inclusion criteria for the review.
  • A small fraction (2%) of studies utilized external datasets for TL.
  • Few studies (5%) addressed multi-site collaborations with privacy considerations.

Conclusions:

  • Actionable TL with structured medical data requires careful selection of source data and models.
  • Choosing appropriate TL frameworks is crucial for addressing healthcare research challenges.
  • Validating TL models against proper baselines is essential for reliable application in diverse healthcare settings.