Cargando…
Artificial Intelligence in Acute Kidney Injury Risk Prediction
Acute kidney injury (AKI) is a frequent complication in hospitalized patients, which is associated with worse short and long-term outcomes. It is crucial to develop methods to identify patients at risk for AKI and to diagnose subclinical AKI in order to improve patient outcomes. The advances in clin...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141311/ https://www.ncbi.nlm.nih.gov/pubmed/32138284 http://dx.doi.org/10.3390/jcm9030678 |
_version_ | 1783519170292350976 |
---|---|
author | Gameiro, Joana Branco, Tiago Lopes, José António |
author_facet | Gameiro, Joana Branco, Tiago Lopes, José António |
author_sort | Gameiro, Joana |
collection | PubMed |
description | Acute kidney injury (AKI) is a frequent complication in hospitalized patients, which is associated with worse short and long-term outcomes. It is crucial to develop methods to identify patients at risk for AKI and to diagnose subclinical AKI in order to improve patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed for the development of artificial intelligence predictive models of risk estimation in AKI. In this review, we discussed the progress of AKI risk prediction from risk scores to electronic alerts to machine learning methods. |
format | Online Article Text |
id | pubmed-7141311 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-71413112020-04-10 Artificial Intelligence in Acute Kidney Injury Risk Prediction Gameiro, Joana Branco, Tiago Lopes, José António J Clin Med Review Acute kidney injury (AKI) is a frequent complication in hospitalized patients, which is associated with worse short and long-term outcomes. It is crucial to develop methods to identify patients at risk for AKI and to diagnose subclinical AKI in order to improve patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed for the development of artificial intelligence predictive models of risk estimation in AKI. In this review, we discussed the progress of AKI risk prediction from risk scores to electronic alerts to machine learning methods. MDPI 2020-03-03 /pmc/articles/PMC7141311/ /pubmed/32138284 http://dx.doi.org/10.3390/jcm9030678 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Gameiro, Joana Branco, Tiago Lopes, José António Artificial Intelligence in Acute Kidney Injury Risk Prediction |
title | Artificial Intelligence in Acute Kidney Injury Risk Prediction |
title_full | Artificial Intelligence in Acute Kidney Injury Risk Prediction |
title_fullStr | Artificial Intelligence in Acute Kidney Injury Risk Prediction |
title_full_unstemmed | Artificial Intelligence in Acute Kidney Injury Risk Prediction |
title_short | Artificial Intelligence in Acute Kidney Injury Risk Prediction |
title_sort | artificial intelligence in acute kidney injury risk prediction |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141311/ https://www.ncbi.nlm.nih.gov/pubmed/32138284 http://dx.doi.org/10.3390/jcm9030678 |
work_keys_str_mv | AT gameirojoana artificialintelligenceinacutekidneyinjuryriskprediction AT brancotiago artificialintelligenceinacutekidneyinjuryriskprediction AT lopesjoseantonio artificialintelligenceinacutekidneyinjuryriskprediction |