Kavli Affiliate: Li Xin Li
| First 5 Authors: Siqi Li, Xin Li, Kunyu Yu, Di Miao, Mingcheng Zhu
| Summary:
Clinical and biomedical research in low-resource settings often faces
significant challenges due to the need for high-quality data with sufficient
sample sizes to construct effective models. These constraints hinder robust
model training and prompt researchers to seek methods for leveraging existing
knowledge from related studies to support new research efforts. Transfer
learning (TL), a machine learning technique, emerges as a powerful solution by
utilizing knowledge from pre-trained models to enhance the performance of new
models, offering promise across various healthcare domains. Despite its
conceptual origins in the 1990s, the application of TL in medical research has
remained limited, especially beyond image analysis. In our review of TL
applications in structured clinical and biomedical data, we screened 3,515
papers, with 55 meeting the inclusion criteria. Among these, only 2% (one out
of 55) utilized external studies, and 7% (four out of 55) addressed scenarios
involving multi-site collaborations with privacy constraints. To achieve
actionable TL with structured medical data while addressing regional
disparities, inequality, and privacy constraints in healthcare research, we
advocate for the careful identification of appropriate source data and models,
the selection of suitable TL frameworks, and the validation of TL models with
proper baselines.
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