Year: 2020
Type: conference-paper
Abstract: ICD coding is the task of classifying and coding all diagnoses, symptoms and procedures associated with a patient's visit.The process is often manual, extremely time-consuming and expensive for hospitals as clinical interactions are usually recorded in free text medical notes.In this paper, we propose a machine learning model, BERT-XML, for large scale automated ICD coding of EHR notes, utilizing recently developed unsupervised pretraining that have achieved state of the art performance on a variety of NLP tasks.We train a BERT model from scratch on EHR notes, learning with vocabulary better suited for EHR tasks and thus outperform off-the-shelf models.We further adapt the BERT architecture for ICD coding with multi-label attention.We demonstrate the effectiveness of BERT-based models on the large scale ICD code classification task using millions of EHR notes to predict thousands of unique codes.
Authors: Zachariah Zhang, Jingshu Liu, Narges Razavian
Institution: NYU Langone Health
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3103901889