Multi-ethnic Survival Prediction: Transfer Learning with Cox Neural Networks

Extensive collections of personal omics data from large clinical cohorts provide an unprecedented opportunity to develop high-performance machine learning systems for precision medicine. However, most clinical omics data were collected from individuals of European ancestry. Such ancestrally imbalanced data may lead to inaccurate machine learning models for the data-disadvantaged ethnic groups and thus generate new health care disparities. In this work, we develop a transfer learning scheme for survival analysis with multi-ethnic data. We perform machine learning experiments on real and synthetic clinical omics datasets to show that transfer learning can improve the prognostic accuracy of Cox neural network models for data-disadvantaged ethnic groups.