Data Analytics in Acute Kidney Injury Prediction: Opportunities and Challenges-Reference-Cited by-同舟云学术

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Data Analytics in Acute Kidney Injury Prediction: Opportunities and Challenges

Published:2022-02-21 Issue: Volume: Page:
ISSN:
Container-title:2022 Advances in Science and Engineering Technology International Conferences (ASET)
language:
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Author:

Alshamsi Fatima¹,Catacutan Mary Krystelle¹,Aldhanhani Khadeijah²,Alshamsi Helal²,Simsekler Mecit Can Emre²,Anwar Siddiq³

Affiliation:

1. Khalifa University,Department of Biomedical Engineering,Abu Dhabi,UAE

2. Khalifa University,Department of Industrial and Systems Engineering,Abu Dhabi,UAE

3. Sheikh Shakbout Medical City,Department of Medicine,Abu Dhabi,UAE

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9734305/9734306/09735034.pdf?arnumber=9735034

Reference27 articles.

1. Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study

2. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study

3. Early prediction of acquiring acute kidney injury for older inpatients using most effective laboratory test results

4. Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model

5. Nomogram to predict the risk of septic acute kidney injury in the first 24 h of admission: an analysis of intensive care unit data

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