Explainable Transfer Learning for Longitudinal Ophthalmic Studies: A Regression-Based Approach

Music City Center 

Transfer learning (TL) is widely employed to address data inequality in healthcare systems, including ophthalmology and vision health. Various TL approaches are integrated into deep learning frameworks to accommodate complex dependencies, such as longitudinal and image data; however, interpretability remains a significant challenge. This study aims to develop an explainable, regression-based TL method for high-dimensional longitudinal studies. We propose a TL algorithm based on general linear mixed-effects models to transfer useful information from multimodal source domains, enhancing the estimation of fixed-effect coefficients and facilitating variable selection in the target domain while effectively managing complex data dependencies, such as repeated measurements. A simulation study was conducted to validate the predictive performance of the proposed approach.