ArticleScientific reports2022
Development and implementation of patient-level prediction models of end-stage renal disease for type 2 diabetes patients using fast healthcare interoperability resources.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- Implementation and Updating of Clinical Prediction Models: A Systematic Review.Mayo Clinic proceedings. Digital health · 2025Review
- Dynamic survival prediction of end-stage kidney disease using random survival forests for competing risk analysis.Frontiers in medicine · 2024Article
- Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review.JMIR medical informatics · 2023Article
- Interpretable Stratification for Chronic Kidney Disease Progression Based on Time to Event Analysis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- The potential of the Medical Digital Twin in diabetes management: a review.Frontiers in medicine · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop a model to predict the 5-year risk of developing end-stage renal disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) using machine learning (ML). It also aimed to implement the developed algorithms into electronic medical records (EMR) system using Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR). The final dataset used for modeling included 19,159 patients. The medical data were engineered to generate various types of features that were input into the various ML classifiers. The classifier with the best performance was XGBoost, with an area under the receiver operator characteristics curve (AUROC) of 0.95 and area under the precision recall curve (AUPRC) of 0.79 using three-fold cross-validation, compared to other models such as logistic regression, random forest, and support vector machine (AUROC range, 0.929-0.943; AUPRC 0.765-0.792). Serum creatinine, serum albumin, the urine albumin-to-creatinine ratio, Charlson comorbidity index, estimated GFR, and medication days of insulin were features that were ranked high for the ESRD risk prediction. The algorithm was implemented in the EMR system using HL7 FHIR through an ML-dedicated server that preprocessed unstructured data and trained updated data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.