ArticlePLOS digital health2022
Transfer learning for non-image data in clinical research: A scoping review.
Article in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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Who cites it
28 citing papers in PubMed.
- Multi-source data integration through pooling and transfer learning improves generalizability and specialization of deep learning models for ICU mortality and length of stay prediction: a four-database external validation study.Critical care (London, England) · 2026Article
- Few-Shot Transfer Learning for Diabetes Risk Prediction Across Global Populations.Medicina (Kaunas, Lithuania) · 2025Article
- A Multi-Modal Transfer Learning Framework to Reduce Health Disparities in Prostate Adenocarcinoma.bioRxiv : the preprint server for biology · 2025Article
- External validation of a web- and artificial intelligence-based HIV/STI risk assessment tool: performance evaluation using data from Sydney sexual health centre.BMC infectious diseases · 2025Article
- Secondary Prevention of AFAIS: Deploying Traditional Regression, Machine Learning, and Deep Learning Models to Validate and Update CHA2DS2-VASc for 90-Day Recurrence.Journal of clinical medicine · 2025Article
- A Machine Learning Approach to Microcalorimetric Pattern Classification of Pathogens in Synovial Fluid.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Article
- Transfer learning for predicting acute myocardial infarction using electrocardiograms.PLOS digital health · 2025Article
- Gated recurrent unit with decay has real-time capability for postoperative ileus surveillance and offers cross-hospital transferability.Communications medicine · 2025Article
- Subject-Specific Modeling by Domain Adaptation for the Estimation of Subglottal Pressure from Neck-Surface Acceleration Signals.Biomedical signal processing and control · 2025Article
- Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes.PLOS digital health · 2025Article
- Digital pathways connecting social and biological factors to health outcomes and equity.NPJ digital medicine · 2025Article
- Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.Health data science · 2025Review
- Opportunities and Challenges in Soft Tissue Sarcoma Risk Stratification in the Era of Personalised Medicine.Current treatment options in oncology · 2024Review
- Community-engaged artificial intelligence research: A scoping review.PLOS digital health · 2024Article
- Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement.Genome medicine · 2024Article
- Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D's Real-Time Capabilities and Cross-Hospital Transferability.medRxiv : the preprint server for health sciences · 2024Article
- An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia.Journal of clinical medicine · 2024Article
- Article
- A comprehensive analysis and performance evaluation for osteoporosis prediction models.PeerJ. Computer science · 2024Article
- MSDeepAMR: antimicrobial resistance prediction based on deep neural networks and transfer learning.Frontiers in microbiology · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTransfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. METHODS AND
findingsWe systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%).
conclusionsIn this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.