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

Artificial Intelligence (922) Machine Learning (1194) Decision Support for Health Professionals (930) Perioperative Risk Assessment (12) Artificial Intelligence in Surgery and Perioperative Medicine (17) Clinical Information and Decision Making (1125)

Published on in Vol 23, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26843, first published .
Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study

Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study

Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study

Authors of this article:

Syed Asil Ali Naqvi 1 Author Orcid Image ;   Karthik Tennankore 2 Author Orcid Image ;   Amanda Vinson 2 Author Orcid Image ;   Patrice C Roy 1 Author Orcid Image ;   Syed Sibte Raza Abidi 1 Author Orcid Image
Article Authors Cited by (12) Tweetations (16) Metrics

Journals

  1. Chawla R, Balaji S, Alabdali R, Naguib I, Hamed N, Zahran H, Elhoseny M. Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network. Journal of Healthcare Engineering 2022;2022:1 View
  2. Moghadam P, Ahmadi A. A machine learning framework to predict kidney graft failure with class imbalance using Red Deer algorithm. Expert Systems with Applications 2022;210:118515 View
  3. Matsuzaka Y, Yashiro R. Applications of Deep Learning for Drug Discovery Systems with BigData. BioMedInformatics 2022;2(4):603 View
  4. ÖZ T, PEHLİVAN M, PİRİM İ. Predicting Graft Survival in Renal Transplant Patients Using Artificial Intelligence Methods. Forbes Journal of Medicine 2023;4(1):1 View
  5. Ruan X, Wang L, Thongprayoon C, Cheungpasitporn W, Liu H. GRU-D-Weibull: A novel real-time individualized endpoint prediction. Artificial Intelligence in Medicine 2023;146:102696 View
  6. Iseki C, Hayasaka T, Yanagawa H, Komoriya Y, Kondo T, Hoshi M, Fukami T, Kobayashi Y, Ueda S, Kawamae K, Ishikawa M, Yamada S, Aoyagi Y, Ohta Y. Artificial Intelligence Distinguishes Pathological Gait: The Analysis of Markerless Motion Capture Gait Data   Acquired by an iOS Application (TDPT-GT). Sensors 2023;23(13):6217 View
  7. Zhang Y, Deng D, Muller S, Wong G, Yang J. A Multi-Step Precision Pathway for Predicting Allograft Survival in Heterogeneous Cohorts of Kidney Transplant Recipients. Transplant International 2023;36 View
  8. Torres-Gutiérrez M, Lozano-Suárez N, Burgos-Camacho V, Caamaño-Jaraba J, Gómez-Montero J, García-López A, Girón-Luque F. Is Non-Adherence Associated with Adverse Outcomes in Kidney Transplant Recipients? The Role of Non-Adherence as a Risk and Predictor Factor for Graft Loss and Death. Patient Preference and Adherence 2023;Volume 17:2915 View
  9. Du Y, Guan C, Li L, Gan P. Predictive value of machine learning for the risk of acute kidney injury (AKI) in hospital intensive care units (ICU) patients: a systematic review and meta-analysis. PeerJ 2023;11:e16405 View
  10. Wiśnicki K, Donizy P, Hałoń A, Wawrzonkowski P, Janczak D, Krajewska M, Banasik M. Indoleamine 2,3-Dioxygenase 1 (IDO1) in Kidney Transplantation: A Guardian against Rejection. Journal of Clinical Medicine 2023;12(24):7531 View

Books/Policy Documents

  1. Rad J, Tennankore K, Vinson A, Abidi S. Artificial Intelligence in Medicine. View
  2. Suchopárová G, Vidnerová P, Neruda R, Šmíd M. Engineering Applications of Neural Networks. View