A Pre-Trained Clinical Language Model for Acute Kidney Injury

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Pre-Trained contextual language models such as BERT have dramatically improved performances for many NLP tasks recently. However, few have explored BERT on specific medical domain tasks such as early prediction for Acute Kidney Injury (AKI). Since much of the clinical information is contained in clinical notes that are largely unstructured text, in this paper, we present an AKI domain-specific pre-Trained language model based on BERT (AKI-BERT) that could be used to mine the clinical notes for AKI early prediction. Our experiments on MIMIC-III dataset demonstrate that AKI-BERT can yield performance improvements for AKI early prediction.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Healthcare Informatics, ICHI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728153827
DOIs
StatePublished - Nov 2020
Event8th IEEE International Conference on Healthcare Informatics, ICHI 2020 - Virtual, Oldenburg, Germany
Duration: Nov 30 2020Dec 3 2020

Publication series

Name2020 IEEE International Conference on Healthcare Informatics, ICHI 2020

Conference

Conference8th IEEE International Conference on Healthcare Informatics, ICHI 2020
Country/TerritoryGermany
CityVirtual, Oldenburg
Period11/30/2012/3/20

Keywords

  • BERT
  • acute kidney injury
  • clinical decision support
  • natural language processing
  • pre-Trained language model

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Decision Sciences (miscellaneous)
  • Modeling and Simulation
  • Medicine (miscellaneous)
  • Health Informatics
  • Health(social science)

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