Generalized Zero-shot ICD Coding
This addresses the labor-intensive and error-prone manual ICD coding process for healthcare professionals, representing an incremental advance with a novel method for a known bottleneck.
The paper tackles the problem of automatic ICD coding, a multi-label text classification task with a long-tailed label distribution, by proposing a latent feature generation framework for generalized zero-shot learning, achieving an F1 score of 20.91% for zero-shot codes and a 3% absolute improvement in AUC score over previous state-of-the-art on the MIMIC-III dataset.
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.