Literature DB >> 31437966

Stratified Mortality Prediction of Patients with Acute Kidney Injury in Critical Care.

Zhenxing Xu1, Yuan Luo2, Prakash Adekkanattu1, Jessica S Ancker1, Guoqian Jiang3, Richard C Kiefer3, Jennifer A Pacheco2, Luke V Rasmussen2, Jyotishman Pathak1, Fei Wang1.   

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.

Entities:  

Keywords:  Acute Kidney Injury; Critical Care; Forecasting

Mesh:

Year:  2019        PMID: 31437966     DOI: 10.3233/SHTI190264

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  6 in total

Review 1.  Artificial Intelligence in Acute Kidney Injury Risk Prediction.

Authors:  Joana Gameiro; Tiago Branco; José António Lopes
Journal:  J Clin Med       Date:  2020-03-03       Impact factor: 4.241

2.  Diuretic effect of co-administration of furosemide and albumin in comparison to furosemide therapy alone: An updated systematic review and meta-analysis.

Authors:  Tao Han Lee; George Kuo; Chih-Hsiang Chang; Yen Ta Huang; Chieh Li Yen; Cheng-Chia Lee; Pei Chun Fan; Jia-Jin Chen
Journal:  PLoS One       Date:  2021-12-01       Impact factor: 3.240

3.  Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease.

Authors:  Xiaohong Zhang; Siying Chen; Kunmei Lai; Zhimin Chen; Jianxin Wan; Yanfang Xu
Journal:  Ren Fail       Date:  2022-12       Impact factor: 2.606

Review 4.  State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review.

Authors:  Na Hong; Chun Liu; Jianwei Gao; Lin Han; Fengxiang Chang; Mengchun Gong; Longxiang Su
Journal:  JMIR Med Inform       Date:  2022-03-03

5.  An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation.

Authors:  Yihan Zhang; Dong Yang; Zifeng Liu; Xiaodong Zhang; Shaoli Zhou; Ziqing Hei; Chaojin Chen; Mian Ge; Xiang Li; Tongsen Luo; Zhengdong Wu; Chenguang Shi; Bohan Wang; Xiaoshuai Huang
Journal:  J Transl Med       Date:  2021-07-28       Impact factor: 5.531

Review 6.  Does Artificial Intelligence Make Clinical Decision Better? A Review of Artificial Intelligence and Machine Learning in Acute Kidney Injury Prediction.

Authors:  Tao Han Lee; Jia-Jin Chen; Chi-Tung Cheng; Chih-Hsiang Chang
Journal:  Healthcare (Basel)       Date:  2021-11-30
  6 in total

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