Multisource 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 multisource 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.

Hierarchical Adaptive Multitask Learning Framework for Patient Diagnoses and Diagnostic Category Classification The problems a patient suffers from can be summarized in terms of a list of patient diagnoses. The diagnoses are typically organized in a hierarchy (or a lattice structure) in which many different lowlevel diagnoses are covered by one or more diagnostic categories. An interesting machine learning problem is related to learning of a wide range of diagnostic models (at different levels of abstraction) that can automatically assign a diagnosis or a diagnostic category to a specific patient. While one can always approach this problem by learning models for each diagnostic task independently, an interesting open question is how one can leverage the knowledge of a diagnostic hierarchy to improve the classification and outperform independent diagnostic models. In this work, we study this problem by designing a new hierarchical classification learning framework in which multiple diagnostic classification targets are explicitly related via diagnostic hierarchy relations. By conducting experiments on MIMICIII data and ICD9 diagnosis hierarchy, we demonstrate that our framework leads to improved classification performance on individual diagnostic tasks when compared to independently learned diagnostic models. This improvement is stronger for diagnoses with a low prior and smaller number of positive training examples.