Machine Learning, Clinical Notes and Knowledge Graphs for Early Prediction of Acute Kidney Injury in the Intensive Care

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Abstract

Acute kidney injury (AKI) is an abrupt decrease of kidney function which is common in the intensive care. Many AKI prediction models have been proposed, but an analysis of what is the added value of clinical notes and medical terminologies has not yet been conducted. We developed and internally validated a model to predict AKI that includes not only clinical variables, but also clinical notes and medical terminologies. Our results were overall good (AUROC > 0.80). The best model used only clinical variables (AUROC 0.899).

Original languageEnglish
Pages (from-to)329-332
Number of pages4
JournalStudies in health technology and informatics
Volume289
DOIs
Publication statusPublished - 14 Jan 2022

Keywords

  • Acute kidney injury
  • ICU
  • clinical models
  • natural language processing

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