An Encoder-Decoder Model for ICD-10 Coding of Death Certificates
This work addresses the task of medical concept coding for death certificates, which is incremental as it applies an existing neural method to a specific domain with enhancements.
The authors tackled the problem of automatically assigning ICD-10 codes to death certificate text using a recurrent neural network encoder-decoder model, achieving an F-measure of 85.01% on a standard benchmark, which significantly improved over the average participant score of 62.2%.
Information extraction from textual documents such as hospital records and healthrelated user discussions has become a topic of intense interest. The task of medical concept coding is to map a variable length text to medical concepts and corresponding classification codes in some external system or ontology. In this work, we utilize recurrent neural networks to automatically assign ICD-10 codes to fragments of death certificates written in English. We develop end-to-end neural architectures directly tailored to the task, including basic encoder-decoder architecture for statistical translation. In order to incorporate prior knowledge, we concatenate cosine similarities vector among the text and dictionary entry to the encoded state. Being applied to a standard benchmark from CLEF eHealth 2017 challenge, our model achieved F-measure of 85.01% on a full test set with significant improvement as compared to the average score of 62.2% for all official participants approaches.