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      Artificial Intelligence in Acute Kidney Injury Risk Prediction

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          Abstract

          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.

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          KDIGO Clinical Practice Guidelines for Acute Kidney Injury

          Arif Khwaja, A. Khwaja (2012)
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            Acute kidney injury, mortality, length of stay, and costs in hospitalized patients.

            Glenn Chertow, Elisabeth Burdick, Melissa M Honour … (2005)
            The marginal effects of acute kidney injury on in-hospital mortality, length of stay (LOS), and costs have not been well described. A consecutive sample of 19,982 adults who were admitted to an urban academic medical center, including 9210 who had two or more serum creatinine (SCr) determinations, was evaluated. The presence and degree of acute kidney injury were assessed using absolute and relative increases from baseline to peak SCr concentration during hospitalization. Large increases in SCr concentration were relatively rare (e.g., >or=2.0 mg/dl in 105 [1%] patients), whereas more modest increases in SCr were common (e.g., >or=0.5 mg/dl in 1237 [13%] patients). Modest changes in SCr were significantly associated with mortality, LOS, and costs, even after adjustment for age, gender, admission International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis, severity of illness (diagnosis-related group weight), and chronic kidney disease. For example, an increase in SCr >or=0.5 mg/dl was associated with a 6.5-fold (95% confidence interval 5.0 to 8.5) increase in the odds of death, a 3.5-d increase in LOS, and nearly 7500 dollars in excess hospital costs. Acute kidney injury is associated with significantly increased mortality, LOS, and costs across a broad spectrum of conditions. Moreover, outcomes are related directly to the severity of acute kidney injury, whether characterized by nominal or percentage changes in serum creatinine.
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              Artificial intelligence in medicine.

              Johanne Tremblay, Pavel Hamet (2017)
              Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Clin Med
                Journal ID (iso-abbrev): J Clin Med
                Journal ID (publisher-id): jcm
                Title: Journal of Clinical Medicine
                Publisher: MDPI
                ISSN (Electronic): 2077-0383
                Publication date (Electronic): 03 March 2020
                Publication date Collection: March 2020
                Volume: 9
                Issue: 3
                Electronic Location Identifier: 678
                Affiliations
                [1 ]Division of Nephrology and Renal Transplantation, Department of Medicine, Centro Hospitalar Lisboa Norte, EPE, Av. Prof. Egas Moniz, 1649-035 Lisboa, Portugal; jalopes93@ 123456hotmail.com
                [2 ]Department of Medicine, Centro Hospitalar Lisboa Norte, EPE, Av. Prof. Egas Moniz, 1649-035 Lisboa, Portugal; tiagobranco.md@ 123456gmail.com
                Author notes
                Author information
                https://orcid.org/0000-0001-6143-461X
                https://orcid.org/0000-0001-5563-5132
                Article
                Publisher ID: jcm-09-00678
                DOI: 10.3390/jcm9030678
                PMC ID: 7141311
                PubMed ID: 32138284
                SO-VID: e0456001-efc8-4f9a-a363-68180edcf7f1
                Copyright © © 2020 by the authors.
                License:

                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/).

                History
                Date received : 27 January 2020
                Date accepted : 28 February 2020
                Categories
                Subject: Review

                Keywords: acute kidney injury,risk prediction,artificial intelligence
                Data availability:
                Keywords: acute kidney injury, risk prediction, artificial intelligence

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