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Acute kidney injury (AKI) is a common clinical syndrome characterized by asudden episode of kidney failure or kidney damage within a few hours or a fewdays. Accurate early prediction of AKI for patients in ICU who are more likelythan others to have AKI can enable timely interventions, and reduce thecomplications of AKI. Much of the clinical information relevant to AKI iscaptured in clinical notes that are largely unstructured text and requiresadvanced natural language processing (NLP) for useful information extraction.On the other hand, pre-trained contextual language models such as BidirectionalEncoder Representations from Transformers (BERT) have improved performances formany NLP tasks in general domain recently. However, few have explored BERT ondisease-specific medical domain tasks such as AKI early prediction. In thispaper, we try to apply BERT to specific diseases and present an AKIdomain-specific pre-trained language model based on BERT (AKI-BERT) that couldbe used to mine the clinical notes for early prediction of AKI. AKI-BERT is aBERT model pre-trained on the clinical notes of patients having risks for AKI.Our experiments on Medical Information Mart for Intensive Care III (MIMIC-III)dataset demonstrate that AKI-BERT can yield performance improvements for earlyAKI prediction, thus expanding the utility of the BERT model from generalclinical domain to disease-specific domain.

AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney Injury