  The International Classification of Diseases (ICD) is a list of
classification codes for the diagnoses. Automatic ICD coding is in high demand
as the manual coding can be labor-intensive and error-prone. It is a
multi-label text classification task with extremely long-tailed label
distribution, making it difficult to perform fine-grained classification on
both frequent and zero-shot codes at the same time. In this paper, we propose a
latent feature generation framework for generalized zero-shot ICD coding, where
we aim to improve the prediction on codes that have no labeled data without
compromising the performance on seen codes. Our framework generates pseudo
features conditioned on the ICD code descriptions and exploits the ICD code
hierarchical structure. To guarantee the semantic consistency between the
generated features and real features, we reconstruct the keywords in the input
documents that are related to the conditioned ICD codes. To the best of our
knowledge, this works represents the first one that proposes an adversarial
generative model for the 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. We also show that the
framework improves the performance on few-shot codes.
