Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study.
Background: Kidney transplantation is the optimal treatment for patients with end-stage renal disease. Short- and long-term kidney graft survival is influenced by a number of donor and recipient factors. Predicting the success of kidney transplantation is important for optimizing kidney allocation.O...
| Publicado en: | Journal of Medical Internet Research Vol. 23; no. 8 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
JMIR Publications Inc.
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152246745&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152246745 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14394456 DNC jtl: Journal of Medical Internet Research issn: 14394456 maglogo: N pubinfo: dt: Aug2021 vid: 23 iid: 8 pid: 21567 pub: JMIR Publications Inc. place: Toronto, Ontario artinfo: ui: 152246745 152246745 NLM34448704 152246745 10.2196/26843 NLM34448704 152246745 ppct: 1 formats: tig: atl: Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study. aug: au: Naqvi, Syed Asil Ali Tennankore, Karthik Vinson, Amanda Roy, Patrice C Abidi, Syed Sibte Raza affil: 1 Department of Computer Science, Dalhousie University, Halifax, NS, Canada sug: subj: Kidney Transplantation Graft Survival Kidney Transplant Donors Human ab: Background: Kidney transplantation is the optimal treatment for patients with end-stage renal disease. Short- and long-term kidney graft survival is influenced by a number of donor and recipient factors. Predicting the success of kidney transplantation is important for optimizing kidney allocation.Objective: The aim of this study was to predict the risk of kidney graft failure across three temporal cohorts (within 1 year, within 5 years, and after 5 years following a transplant) based on donor and recipient characteristics. We analyzed a large data set comprising over 50,000 kidney transplants covering an approximate 20-year period.Methods: We applied machine learning-based classification algorithms to develop prediction models for the risk of graft failure for three different temporal cohorts. Deep learning-based autoencoders were applied for data dimensionality reduction, which improved the prediction performance. The influence of features on graft survival for each cohort was studied by investigating a new nonoverlapping patient stratification approach.Results: Our models predicted graft survival with area under the curve scores of 82% within 1 year, 69% within 5 years, and 81% within 17 years. The feature importance analysis elucidated the varying influence of clinical features on graft survival across the three different temporal cohorts.Conclusions: In this study, we applied machine learning to develop risk prediction models for graft failure that demonstrated a high level of prediction performance. Acknowledging that these models performed better than those reported in the literature for existing risk prediction tools, future studies will focus on how best to incorporate these prediction models into clinical care algorithms to optimize the long-term health of kidney recipients. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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