ArticleMathematical biosciences2019
Predicting outcomes of chronic kidney disease from EMR data based on Random Forest Regression.
Article in Mathematical biosciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.Renal failure · 2024Pooled it
- Pooled it
- A Novel Kidney Failure Prediction Model in Individuals With CKD: Impact of Serum Bilirubin Levels.The Journal of clinical endocrinology and metabolism · 2025Article
- Optimizing use of an electronic medical record system for quality improvement initiatives in hemodialysis: Review of a single center experience.Hemodialysis international. International Symposium on Home Hemodialysis · 2025Review
- Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression.Nature genetics · 2024Article
- Advanced CKD detection through optimized metaheuristic modeling in healthcare informatics.Scientific reports · 2024Article
- Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research.Diagnostics (Basel, Switzerland) · 2024Review
- Unveiling the biological effects of radio-frequency and extremely-low frequency electromagnetic fields on the central nervous system performance.BioImpacts : BI · 2024Review
- Predictive modeling of biomedical temporal data in healthcare applications: review and future directions.Frontiers in physiology · 2024Review
- ESKD Risk Prediction Model in a Multicenter Chronic Kidney Disease Cohort in China: A Derivation, Validation, and Comparison Study.Journal of clinical medicine · 2023Article
- Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality.PLOS digital health · 2023Article
- An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages.Frontiers in medicine · 2023Article
- Artificial intelligence in peritoneal dialysis: general overview.Renal failure · 2022Article
- Machine learning to predict end stage kidney disease in chronic kidney disease.Scientific reports · 2022Article
- Prediction models used in the progression of chronic kidney disease: A scoping review.PloS one · 2022Article
- Comparison of Different Machine Learning Techniques to Predict Diabetic Kidney Disease.Journal of healthcare engineering · 2022Article
- Machine Learning Improves Upon Clinicians' Prediction of End Stage Kidney Disease.Frontiers in medicine · 2022Article
- A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign.Pattern recognition · 2022Article
- An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction.Frontiers in public health · 2022Article
- Air, Land, and Sea: Gene-Environment Interaction in Chronic Disease.The American journal of medicine · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Chronic kidney disease (CKD) is prevalent across the world, and kidney function is well defined by an estimated glomerular filtration rate (eGFR). The progression of kidney disease can be predicted if the future eGFR can be accurately estimated using predictive analytics. In this study, we developed and validated a prediction model of eGFR by data extracted from a regional health system. This dataset includes demographic, clinical and laboratory information from primary care clinics. The model was built using Random Forest regression and evaluated using Goodness-of-fit statistics and discrimination metrics. After data preprocessing, the patient cohort for model development and validation contained 61,740 patients. The final model included eGFR, age, gender, body mass index (BMI), obesity, hypertension, and diabetes, which achieved a mean coefficient of determination of 0.95. The estimated eGFRs were used to classify patients into CKD stages with high macro-averaged and micro-averaged metrics. In conclusion, a model using real-world electronic medical records (EMR) data can accurately predict future kidney functions and provide clinical decision support.
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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.