@inproceedings{53ffbcb1c6fe40418817bbdf22f3f788,
title = "Stratified mortality prediction of patients with acute kidney injury in critical care",
abstract = "Acute Kidney Injury (AKI) is the most common cause of organ dysfunction in critically ill adults and prior studies have shown AKI is associated with a significant increase of the mortality risk. Early prediction of the mortality risk for AKI patients can help clinical decision makers better understand the patient condition in time and take appropriate actions. However, AKI is a heterogeneous disease and its cause is complex, which makes such predictions a challenging task. In this paper, we investigate machine learning models for predicting the mortality risk of AKI patients who are stratified according to their AKI stages. With this setup we demonstrate the stratified mortality prediction performance of patients with AKI is better than the results obtained on the mixed population.",
keywords = "Acute Kidney Injury, Critical Care, Forecasting",
author = "Zhenxing Xu and Yuan Luo and Prakash Adekkanattu and Ancker, {Jessica S.} and Guoqian Jiang and Kiefer, {Richard C.} and Pacheco, {Jennifer A.} and Rasmussen, {Luke V.} and Jyotishman Pathak and Fei Wang",
note = "Publisher Copyright: {\textcopyright} 2019 International Medical Informatics Association (IMIA) and IOS Press.; 17th World Congress on Medical and Health Informatics, MEDINFO 2019 ; Conference date: 25-08-2019 Through 30-08-2019",
year = "2019",
month = aug,
day = "21",
doi = "10.3233/SHTI190264",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "462--466",
editor = "Brigitte Seroussi and Lucila Ohno-Machado and Lucila Ohno-Machado and Brigitte Seroussi",
booktitle = "MEDINFO 2019",
}