Improving cancer risk prediction for underrepresented groups using transfer learning

Music City Center 

Using risk prediction models tailored to specific populations to support medical decision making has the potential to improve patient outcomes, but developing such models for underrepresented groups is challenging due to limited sample sizes. In such cases, borrowing information from models developed for the majority population may enhance performance. We compare multiple approaches for improving prediction in an underrepresented target population by leveraging source and target data including regularized regression and pre-trained neural networks. Using simulations, we assess performance across varying degrees of departure between the covariate distribution and model architecture in the source and target populations. We apply these methods in the context of breast cancer risk prediction. Our findings provide insights into strategies for improving prediction in data-limited populations.

risk prediction

transfer learning

machine learning

health equity 

Health Policy Statistics Section