MACHINE LEARNING MODELS FOR PREDICTING ACUTE KIDNEY INJURY: A SYSTEMATIC REVIEW

被引：0
|
作者：
Vagliano, Iacopo [1 ]
Chesnaye, Nicholas [1 ,3 ]
Leopold, Jan Hendrik [1 ,2 ]
Jager, Kitty J. [1 ,3 ]
Abu Hanna, Ameen [1 ,2 ]
Schut, Martijn C. [1 ]
机构：
[1] Univ Amsterdam, Amsterdam UMC, Dept Med Informat, Amsterdam, Netherlands
[2] ERA EDTA Registry, Amsterdam, Netherlands
[3] Amsterdam Publ Hlth Res Inst, Amsterdam, Netherlands
来源：
NEPHROLOGY DIALYSIS TRANSPLANTATION | 2021年 / 36卷
关键词：
D O I：
暂无
中图分类号：
R3 [基础医学]; R4 [临床医学];
学科分类号：
1001 ; 1002 ; 100602 ;
摘要：
MO360
引用
收藏
页数：1
相关论文
共 50 条
  • [41] Machine learning models for predicting pre-eclampsia: a systematic review protocol
    Ranjbar, Amene
    Taeidi, Elham
    Mehrnoush, Vahid
    Roozbeh, Nasibeh
    Darsareh, Fatemeh
    BMJ OPEN, 2023, 13 (09):
  • [42] A SYSTEMATIC REVIEW OF MACHINE LEARNING MODELS FOR PREDICTING OUTCOMES OF STROKE WITH STRUCTURED DATA
    Wang, W.
    Kiik, M.
    Peek, N.
    Curcin, V.
    Marshall, I.
    Rudd, A.
    Wang, Y.
    Douiri, A.
    Wolfe, C.
    Bray, B.
    INTERNATIONAL JOURNAL OF STROKE, 2020, 15 (1_SUPPL) : 641 - 641
  • [43] A systematic review of machine learning models for predicting outcomes of stroke with structured data
    Wang, Wenjuan
    Kiik, Martin
    Peek, Niels
    Curcin, Vasa
    Marshall, Iain J.
    Rudd, Anthony G.
    Wang, Yanzhong
    Douiri, Abdel
    Wolfe, Charles D.
    Bray, Benjamin
    PLOS ONE, 2020, 15 (06):
  • [44] Machine Learning for Predicting Stillbirth: A Systematic Review
    Li, Qingyuan
    Li, Pan
    Chen, Junyu
    Ren, Ruyu
    Ren, Ni
    Xia, Yinyin
    REPRODUCTIVE SCIENCES, 2024, : 1388 - 1398
  • [45] Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis
    Song, Zhe
    Yang, Zhenyu
    Hou, Ming
    Shi, Xuedong
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2022, 9
  • [46] Validated risk prediction models for outcomes of acute kidney injury: a systematic review
    Fateme Nateghi Haredasht
    Laban Vanhoutte
    Celine Vens
    Hans Pottel
    Liesbeth Viaene
    Wouter De Corte
    BMC Nephrology, 24
  • [47] Validated risk prediction models for outcomes of acute kidney injury: a systematic review
    Haredasht, Fateme Nateghi
    Vanhoutte, Laban
    Vens, Celine
    Pottel, Hans
    Viaene, Liesbeth
    De Corte, Wouter
    BMC NEPHROLOGY, 2023, 24 (01)
  • [48] Prevalence and predicting factors of acute kidney injury due to methanol intoxication; a systematic review
    Gharaeikhezri, Mohammad Hesam
    Fattahi, Farinaz
    Karami, Pegah
    Ghahremani, Arash Izadpanah
    Rezaei, Bareza
    Rafiei, Hooman
    Khodabandeh, Hamidreza
    Oskui, Aisan Ghasemi
    Gharebakhshi, Farshad
    Rezaei, Mohammad Reza
    JOURNAL OF RENAL INJURY PREVENTION, 2024, 13 (02):
  • [49] Predicting pediatric cardiac surgery-associated acute kidney injury using machine learning
    Nagy, Matthew
    Onder, Ali Mirza
    Rosen, David
    Mullett, Charles
    Morca, Ayse
    Baloglu, Orkun
    PEDIATRIC NEPHROLOGY, 2024, 39 (04) : 1263 - 1270
  • [50] Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury
    Jiang, Meng
    Pan, Chun-qiu
    Li, Jian
    Xu, Li-gang
    Li, Chang-li
    RENAL FAILURE, 2023, 45 (01)
12345