Early prediction of acute kidney injury in critical care setting using clinical notes

Yikuan Li, Liang Yao, Chengsheng Mao, Anand Srivastava, Xiaoqian Jiang, Yuan Luo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will allow for testing of novel strategies to prevent or reduce the complications of AKI. We developed data-driven prediction models to estimate the risk of new AKI onset. We generated models from clinical notes within the first 24 hours following intensive care unit (ICU) admission extracted from Medical Information Mart for Intensive Care III (MIMIC-III). From the clinical notes, we generated clinically meaningful word and concept representations and embeddings, respectively. Five supervised learning classifiers and knowledge-guided deep learning architecture were used to construct prediction models. The best configuration yielded a competitive AUC of 0.779. Our work suggests that natural language processing of clinical notes can be applied to assist clinicians in identifying the risk of incident AKI onset in critically ill patients upon admission to the ICU.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Nov 6 2018

Keywords

  • Machine Learning
  • Medical Decision Making
  • Natural Language Processing
  • Unified Medical Language System

ASJC Scopus subject areas

  • General

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