This paper is in the following e-collection/theme issue:

Machine Learning (1999) Decision Support for Health Professionals (1490)

Published on in Vol 8, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20578, first published .
Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study

Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study

Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study

Authors of this article:

Kaixiang Sheng1 Author Orcid Image ;   Ping Zhang1 Author Orcid Image ;   Xi Yao1 Author Orcid Image ;   Jiawei Li1 Author Orcid Image ;   Yongchun He1 Author Orcid Image ;   Jianghua Chen1 Author Orcid Image
Article Authors Cited by (11) Tweetations (1) Metrics

Journals

  1. Díez-Sanmartín C, Sarasa-Cabezuelo A, Andrés Belmonte A. The impact of artificial intelligence and big data on end-stage kidney disease treatments. Expert Systems with Applications 2021;180:115076 View
  2. Liu Y, Yang C, Chiu P, Lin H, Lo C, Lai A, Chang C, Lee O. Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study. Journal of Medical Internet Research 2021;23(9):e27098 View
  3. Rankin S, Han L, Scherzer R, Tenney S, Keating M, Genberg K, Rahn M, Wilkins K, Shlipak M, Estrella M. A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation. Kidney360 2022;3(9):1556 View
  4. Bailey A, Eltawil M, Gohel S, Byham-Gray L. Machine learning models using non-linear techniques improve the prediction of resting energy expenditure in individuals receiving hemodialysis. Annals of Medicine 2023;55(2) View
  5. Lee W, Fang Y, Chang W, Hsiao K, Shia B, Chen M, Tsai M. Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients. Scientific Reports 2023;13(1) View
  6. Chang T, Chen Y, Lu H, Wu J, Mak K, Yu C. Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning. Medicine 2024;103(7):e37112 View
  7. Okada H, Ono A, Tomori K, Inoue T, Hanafusa N, Sakai K, Narita I, Moriyama T, Isaka Y, Fukami K, Itano S, Kanda E, Kashihara N, McGrowder D. Development of a prognostic risk score to predict early mortality in incident elderly Japanese hemodialysis patients. PLOS ONE 2024;19(4):e0302101 View
  8. Díez-Sanmartín C, Sarasa Cabezuelo A, Andrés Belmonte A. Ensemble of machine learning techniques to predict survival in kidney transplant recipients. Computers in Biology and Medicine 2024;180:108982 View
  9. Motofelea A, Mihaescu A, Olariu N, Marc L, Chisavu L, Pop G, Crintea A, Jura A, Ivan V, Apostol A, Schiller A. Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review. Applied Sciences 2025;15(10):5776 View
  10. Koh B, Sridharan S, Venkataraman N, Oh H, Roy D. Predicting mortality in elderly chronic kidney disease patients using algorithmic risk assessment: a Southeast Asian perspective. BMC Nephrology 2025;26(1) View

Books/Policy Documents

  1. Monaghan C, Looper K, Usvyat L. Technological Advances in Care of Patients with Kidney Diseases. View