Automated ICD coding for coronary heart diseases by a deep learning method.

Automated ICD coding via machine learning that focuses on some specific diseases has been a hot topic. As one of the leading causes of death, coronary heart diseases (CHD) have seldom been specifically studied by related research, probably due to lack of data concretely targeting at the diseases. Based on Fuwai-CHD and MIMIC-III-CHD, which are a private dataset from Fuwai Hospital and the CHD-related subset of a public dataset named MIMIC-III respectively, this study aimed at automated CHD coding by a deep learning method, which mainly consists of three modules. The first is a variant module responsible for encoding clinical text. In the module, we fine-tuned variants with masked language model on clinical text, and proposed a truncation method to tackle the problem that variants generally cannot handle sequences containing more than 512 tokens. The second is a ord2vec module for encoding code titles and the third is a label-ention module for integrating the embeddings of clinical text and code titles. In short, we named the method . We compared against some widely studied baselines, and found that performed best in most of the coding missions. Specifically, reached a 1 of 96.2% and a of 98.9% for the top-100 most frequent codes in Fuwai-CHD, which covered 89.2% of the total code occurrences. When predicting the top-50 most frequent codes in MIMIC-III-CHD, reached a 1 of 40.5% and a of 66.1%. Moreover, was capable of locating informative tokens from clinical text for predicting the target codes. In summary, can not only suggest CHD codes accurately, but also possess robust interpretability, hence has great potential in facilitating CHD coding in practice.