TY - GEN
T1 - Data Analytics in Acute Kidney Injury Prediction
T2 - 2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
AU - Alshamsi, Fatima
AU - Catacutan, Mary Krystelle
AU - Aldhanhani, Khadeijah
AU - Alshamsi, Helal
AU - Simsekler, Mecit Can Emre
AU - Anwar, Siddiq
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Acute Kidney Injury
KW - Artificial Intelligence
KW - Big Data
KW - Data Analytics
KW - EHR
KW - Kidney Care
KW - Machine Learning
KW - Nephrology
KW - Systematic Literature Review
UR - http://www.scopus.com/inward/record.url?scp=85128432628&partnerID=8YFLogxK
U2 - 10.1109/ASET53988.2022.9735034
DO - 10.1109/ASET53988.2022.9735034
M3 - Conference contribution
AN - SCOPUS:85128432628
T3 - 2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
BT - 2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 February 2022 through 24 February 2022
ER -
