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

Artificial Intelligence (1216) Machine Learning (1401) Decision Support for Health Professionals (1147) Perioperative Risk Assessment (13) Artificial Intelligence in Surgery and Perioperative Medicine (24) Clinical Information and Decision Making (1340)

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 Naqvi1 Author Orcid Image ;   Karthik Tennankore2 Author Orcid Image ;   Amanda Vinson2 Author Orcid Image ;   Patrice C Roy1 Author Orcid Image ;   Syed Sibte Raza Abidi1 Author Orcid Image
Article Authors Cited by (19) 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
  11. Takkavatakarn K, Oh W, Cheng E, Nadkarni G, Chan L. Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4. BMC Nephrology 2023;24(1) View
  12. Schapranow M, Bayat M, Rasheed A, Naik M, Graf V, Schmidt D, Budde K, Cardinal H, Sapir-Pichhadze R, Fenninger F, Sherwood K, Keown P, Günther O, Pandl K, Leiser F, Thiebes S, Sunyaev A, Niemann M, Schimanski A, Klein T. NephroCAGE—German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study. JMIR Research Protocols 2023;12:e48892 View
  13. Wang J, Lu C, Wang J, Wang Y, Bi H, Zheng J, Ding X. Necroptosis-related genes allow novel insights into predicting graft loss and diagnosing delayed graft function in renal transplantation. Genomics 2024;116(2):110778 View
  14. Ramalhete L, Almeida P, Ferreira R, Abade O, Teixeira C, Araújo R. Revolutionizing Kidney Transplantation: Connecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare—From Algorithms to Allografts. BioMedInformatics 2024;4(1):673 View
  15. Akl A, Lomatayo B, Adejum O. The evolution of artificial intelligence (AI) in nephrology: advantages and disadvantages. Urology & Nephrology Open Access Journal 2023;11(3):103 View
  16. Lukomski L, Pisula J, Wagner T, Sabov A, Große Hokamp N, Bozek K, Popp F, Kann M, Kurschat C, Becker J, Bruns C, Thomas M, Stippel D. First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation. Journal of Nephrology 2024 View
  17. Ding H, Feng X, Yang Q, Yang Y, Zhu S, Ji X, Kang Y, Shen J, Zhao M, Xu S, Ning G, Xu Y. A risk prediction model for efficient intubation in the emergency department: A 4‐year single‐center retrospective analysis. Journal of the American College of Emergency Physicians Open 2024;5(3) 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