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Clinical interactions are initially recorded and documented in free textmedical notes. ICD coding is the task of classifying and coding all diagnoses,symptoms and procedures associated with a patient's visit. The process is oftenmanual and extremely time-consuming and expensive for hospitals. In this paper,we propose a machine learning model, BERT-XML, for large scale automated ICDcoding from EHR notes, utilizing recently developed unsupervised pretrainingthat have achieved state of the art performance on a variety of NLP tasks. Wetrain a BERT model from scratch on EHR notes, learning with vocabulary bettersuited for EHR tasks and thus outperform off-the-shelf models. We adapt theBERT architecture for ICD coding with multi-label attention. While other worksfocus on small public medical datasets, we have produced the first large scaleICD-10 classification model using millions of EHR notes to predict thousands ofunique ICD codes.

BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining