ArticlePloS one2020
Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 59 citations in OpenAlex.
- Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.Renal failure · 2024Pooled it
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis.BMC medical informatics and decision making · 2022Pooled it
- Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges.Chronic diseases and translational medicine · 2025Review
- A machine learning model for predicting worsening renal function using one-year time series data in patients with type 2 diabetes.Journal of diabetes investigation · 2025Article
- Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage.PeerJ. Computer science · 2025Article
- Review
- Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach.BMJ health & care informatics · 2024Article
- Predicting CKD progression using time-series clustering and light gradient boosting machines.Scientific reports · 2024Article
- Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4.BMC nephrology · 2023Article
- Prediction of chronic kidney disease progression using recurrent neural network and electronic health records.Scientific reports · 2023Article
- Machine Learning Models for the Prediction of Renal Failure in Chronic Kidney Disease: A Retrospective Cohort Study.Diagnostics (Basel, Switzerland) · 2022Article
- Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.Advances in chronic kidney disease · 2022Review
- Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease.Scientific reports · 2022Article
- Prediction models used in the progression of chronic kidney disease: A scoping review.PloS one · 2022Article
- Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma.Frontiers in endocrinology · 2022Article
- Comparison of Different Machine Learning Techniques to Predict Diabetic Kidney Disease.Journal of healthcare engineering · 2022Article
- Health information technology to improve care for people with multiple chronic conditions.Health services research · 2021Review
- The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients.Healthcare (Basel, Switzerland) · 2021Article
- Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan.Healthcare (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient's well-being and for successful management of the condition. In this paper, we explore the possibilities of creating forecasting models to predict the onset of RRT 3, 6, and 12 months from the time of the patient's first diagnosis with CKD, using only the comorbidities data from National Health Insurance from Taiwan. The goal of this study was to see whether a limited amount of data (including comorbidities but not considering laboratory values which are expensive to obtain in low- and medium-income countries) can provide a good basis for such predictive models. On the other hand, in developed countries, such models could allow policy-makers better planning and allocation of resources for treatment. Using data from 8,492 patients, we obtained the area under the receiver operating characteristic curve (AUC) of 0.773 for predicting RRT within 12 months from the time of CKD diagnosis. The results also show that there is no additional advantage in focusing only on patients with diabetes in terms of prediction performance. Although these results are not as such suitable for adoption into clinical practice, the study provides a strong basis and a variety of approaches for future studies of forecasting models in healthcare.
Indexed as
Identifiers
What OpenQuestion holds
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.