Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit

Eric R Gottlieb; Mathew Samuel; Joseph V Bonventre; Leo A Celi; Heather Mattie

doi:10.1053/j.ackd.2022.06.005

Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit

Adv Chronic Kidney Dis. 2022 Sep;29(5):431-438. doi: 10.1053/j.ackd.2022.06.005.

Authors

Eric R Gottlieb¹, Mathew Samuel², Joseph V Bonventre³, Leo A Celi⁴, Heather Mattie⁵

Affiliations

¹ Renal Section, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA. Electronic address: ergottlieb@bwh.harvard.edu.
² MIT Critical Data, Cambridge, MA.
³ Renal Section, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA.
⁴ Harvard Medical School, Boston, MA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA; MIT Critical Data, Cambridge, MA; Harvard T.H. Chan School of Public Health, Boston, MA; Beth Israel Deaconess Medical Center, Boston, MA.
⁵ Harvard T.H. Chan School of Public Health, Boston, MA.

Abstract

Machine learning is the field of artificial intelligence in which computers are trained to make predictions or to identify patterns in data through complex mathematical algorithms. It has great potential in critical care to predict outcomes, such as acute kidney injury, and can be used for prognosis and to suggest management strategies. Machine learning can also be used as a research tool to advance our clinical and biochemical understanding of acute kidney injury. In this review, we introduce basic concepts in machine learning and review recent research in each of these domains.

Keywords: AKI prediction; Algorithms; Artificial intelligence; ICU Nephrology; Machine learning.

Publication types

Review
Research Support, N.I.H., Extramural

MeSH terms

Acute Kidney Injury* / diagnosis
Acute Kidney Injury* / therapy
Artificial Intelligence*
Critical Care
Humans
Intensive Care Units
Machine Learning

Abstract

Publication types

MeSH terms

Grants and funding