Data Analytics in Acute Kidney Injury Prediction: Opportunities and Challenges

Fatima Alshamsi, Mary Krystelle Catacutan, Khadeijah Aldhanhani, Helal Alshamsi, Mecit Can Emre Simsekler, Siddiq Anwar

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    Acute Kidney Injury (AKI) is a common medical condition with a high mortality rate. The incidence of AKI is exceptionally high in hospitalized patients, particularly those suffering from acute illness or postoperative patients. As AKI impacts both patient and financial outcomes, there has been a keen interest the disease. In recent years, AKI and big data synergies have been explored, particularly through electronic health records (EHR), ideal for AKI risk prediction. Due to the massive amount of data in EHR, machine learning (ML) models for data analytics are slowly rising. The application of ML is a promising approach due to its ability to collect EHR data and make predictions on AKI onset accordingly, instead of relying on independent health records. This systematic review aims to identify the opportunities and challenges that arise in integrating data analytics in AKI prediction.

    Original languageBritish English
    Title of host publication2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781665418010
    DOIs
    StatePublished - 2022
    Event2022 Advances in Science and Engineering Technology International Conferences, ASET 2022 - Dubai, United Arab Emirates
    Duration: 21 Feb 202224 Feb 2022

    Publication series

    Name2022 Advances in Science and Engineering Technology International Conferences, ASET 2022

    Conference

    Conference2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
    Country/TerritoryUnited Arab Emirates
    CityDubai
    Period21/02/2224/02/22

    Keywords

    • Acute Kidney Injury
    • Artificial Intelligence
    • Big Data
    • Data Analytics
    • EHR
    • Kidney Care
    • Machine Learning
    • Nephrology
    • Systematic Literature Review

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