Multi-source Ensemble Transfer Approach for Medical Text Auxiliary Diagnosis

In medical text auxiliary diagnosis systems, there exists some problems including few labeled samples, imbalanced classes and domains are related but different. Taking advantages of transfer learning, we propose the multi-source transfer learning approach based on ensemble learning to address the above problems. Source data sampling method is designed to ensure the transfer ability of source samples. Then, three classifiers are ensembled to guarantee the robustness. Finally, classifiers from multiple domains are reasonably combined using mutual information to further improve performance. Our approach has been evaluated on the benchmark medical text datasets, and the results show that our approach is superior to the existing algorithms and can meet the requirement of an auxiliary diagnosis in certain extent.

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