Generalized Zero Shot Text Classification for ICD Coding
The International Classification of Diseases (ICD) is a list of classification codes for the diagnoses. Automatic ICD coding is a multi label text classification problem with noisy clinical document inputs and long tailed label distribution, making it difficult for fine grained classification on both frequent and zero shot codes at the same time, i.e. generalized zero shot ICD coding. In this paper, we propose a latent feature generation framework to improve the prediction on unseen codes without compromising the performance on seen codes. Our framework generates semantically meaningful features for zero shot codes by exploiting ICD code hierarchical structure and reconstructing the code relevant keywords with a novel cycle architecture. To the best of our knowledge, this is the first adversarial generative model for generalized zero shot learning on multi label text classification. Extensive experiments demonstrate the effectiveness of our approach. On the public MIMIC III dataset, our methods improve the F1 score from nearly 0 to 20.91% for the zero shot codes, and increase the AUC score by 3% (absolute improvement) from previous state of the art. Code is available at https:/github.com/csong27/gzsl text.
IJCAI