Few-shot ICD coding with knowledge transfer and evidence representation

The task of automatic ICD (International Classification of Diseases) coding involves allocating appropriate ICD codes to electronic health records. Due to the long-tailed distribution of ICD codes, current methods perform poorly on rare diseases, also known as few-shot codes. Consequently, we resort to transfer learning as a solution to address the challenge of ICD coding with limited instances. In our paper, we examine the opportunities in few-shot ICD coding and propose a new solution, the evidence-representation-based meta-network (EPEN). Our model has two key innovations: (i) we design evidence representation for diseases based on the observation that the same disease can have different symptoms among individuals, and (ii) we construct a meta-network to memorize category knowledge from common diseases and apply it to rare diseases. Many experiments show that our EPEN solution performs better than the previous methods for both frequently occurring ICD codes and infrequently occurring ICD codes (few-shot codes). Furthermore, EPEN exhibits improved stability in performance, as evidenced by an improvement in both the mean and range of the F1-score.