ArticleBMC nephrology2022
Predicting the survival of kidney transplantation: design and evaluation of a smartphone-based application.
Article in BMC nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Healthcare applications for elderly: a scoping review and a practical roadmap for developing health apps.BMC medical informatics and decision making · 2026Article
- Mobile health applications for parents of children with Hirschsprung disease: a requirement analysis.European journal of pediatrics · 2026Article
- Predicting triage levels in patients presenting with cardiac-related symptoms: a comparison of supervised machine learning methods.BMC emergency medicine · 2025Article
- Design and Evaluation of ACAFiB-APP, a clinical decision support system for anticoagulant considerations in patients with atrial fibrillation.BMC cardiovascular disorders · 2025Article
- Predictive Factors of Length of Stay in Intensive Care Unit after Coronary Artery Bypass Graft Surgery based on Machine Learning Methods.Archives of academic emergency medicine · 2025Article
- A mobile application for postoperative education of caregivers of children with congenital hypospadias: Requirement analysis.Health science reports · 2024Article
- Smartphone-based application to control and prevent overweight and obesity in children: design and evaluation.BMC medical informatics and decision making · 2023Article
- A smartphone-based self-care application for patients with urinary tract stones: identification of information content and functional capabilities.BMC urology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrediction of graft survival for Kidney Transplantation (KT) is considered a risky task due to the scarcity of donating organs and the use of health care resources. The present study aimed to design and evaluate a smartphone-based application to predict the survival of KT in patients with End-Stage Renal Disease (ESRD).
methodBased on the initial review, a researcher-made questionnaire was developed to assess the information needs of the application through urologists and nephrologists. By using information obtained from the questionnaire, a checklist was prepared, and the information of 513 patients with kidney failure was collected from their records at Sina Urological Research Center. Then, three data mining algorithms were applied to them. The smartphone-based application for the prediction of kidney transplant survival was designed, and a standard usability assessment questionnaire was used to evaluate the designed application.
resultsThree information elements related to the required data in different sections of demographic information, sixteen information elements related to patient clinical information, and four critical capabilities were determined for the design of the smartphone-based application. C5.0 algorithm with the highest accuracy (87.21%) was modeled as the application inference engine. The application was developed based on the PhoneGap framework. According to the participants' scores (urologists and nephrologists) regarding the usability evaluation of the application, it can be concluded that both groups participating in the study could use the program, and they rated the application at a "good" level.
conclusionSince the overall performance or usability of the smartphone-based app was evaluated at a reasonable level, it can be used with certainty to predict kidney transplant survival.
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