Summary of Bridging Data Gaps in Healthcare: a Scoping Review Of Transfer Learning in Biomedical Data Analysis, by Siqi Li et al.
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis
by Siqi Li, Xin Li, Kunyu Yu, Di Miao, Mingcheng Zhu, Mengying Yan, Yuhe Ke, Danny D’Agostino, Yilin Ning, Qiming Wu, Ziwen Wang, Yuqing Shang, Molei Liu, Chuan Hong, Nan Liu
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper reviews transfer learning (TL) applications in structured clinical and biomedical data, aiming to enhance model performance by leveraging knowledge from pre-trained models. The authors screened 3,515 papers, finding only a few that utilized external studies or addressed multi-site collaborations with privacy constraints. To achieve actionable TL, the authors suggest identifying suitable source data and models, selecting appropriate TL frameworks, and validating TL models with proper baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to use machine learning techniques called transfer learning to help medical research in areas with limited resources. By sharing knowledge from previous studies, researchers can train better models using less data. The authors looked at many papers (over 3,500!) and found that most didn’t properly share information or work together across different sites while protecting patient privacy. |
Keywords
* Artificial intelligence * Machine learning * Transfer learning
