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- W3034051081 abstract "Clinical interactions are initially recorded and documented in free text medical notes. 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 and extremely time-consuming and expensive for hospitals. In this paper, we propose a machine learning model, BERT-XML, for large scale automated ICD coding from 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 adapt the BERT architecture for ICD coding with multi-label attention. While other works focus on small public medical datasets, we have produced the first large scale ICD-10 classification model using millions of EHR notes to predict thousands of unique ICD codes." @default.
- W3034051081 created "2020-06-12" @default.
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- W3034051081 date "2020-05-26" @default.
- W3034051081 modified "2023-09-24" @default.
- W3034051081 title "BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining" @default.
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- W3034051081 doi "https://doi.org/10.48550/arxiv.2006.03685" @default.
- W3034051081 hasPublicationYear "2020" @default.
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