ArticleHealthcare (Basel, Switzerland)2021
Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan.
Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Sexual dysfunction in peritoneal dialysis: associated factors and exploratory machine-learning classification.International urology and nephrology · 2026Article
- GERBEHRT: a BERT-based model tailored for German electronic health records - potential in chronic kidney disease prediction.BMC medical informatics and decision making · 2026Article
- Explainable AI machine learning framework for chronic kidney disease prediction utilizing electronic health records.BMC medical informatics and decision making · 2026Review
- Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies.World journal of nephrology · 2026Review
- An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations.Scientific reports · 2026Article
- Decoding Natural Language Processing and Large Language Models in Kidney Health.Clinical journal of the American Society of Nephrology : CJASN · 2026Article
- Public interest in chronic kidney disease and dialysis: a 20-year data analysis.Renal failure · 2025Article
- A Messaging App Empowering Lifestyle Modification in Chronic Kidney Disease (LINE Official Account "Kidney Lifestyle"): Platform Development and Usability Study.JMIR human factors · 2025Article
- Machine learning models for predicting short-term progression in patients with stage 4 chronic kidney disease: a multi-center validation study.Scientific reports · 2025Observational
- Artificial intelligence applications in health insurances: a scoping review.Cost effectiveness and resource allocation : C/E · 2025Review
- Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions.International urology and nephrology · 2025Review
- Synthetic data as an investigative tool in hypertension and renal diseases research.World journal of methodology · 2025Article
- Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification.PeerJ. Computer science · 2025Article
- Cloud-based real-time enhancement for disease prediction using Confluent Cloud, Apache Kafka, feature optimization, and explainable artificial intelligence.PeerJ. Computer science · 2025Article
- Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage.PeerJ. Computer science · 2025Article
- Article
- Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data.PLOS digital health · 2025Article
- Article
- Deep-kidney: an effective deep learning framework for chronic kidney disease prediction.Health information science and systems · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Chronic kidney disease (CKD) represents a heavy burden on the healthcare system because of the increasing number of patients, high risk of progression to end-stage renal disease, and poor prognosis of morbidity and mortality. The aim of this study is to develop a machine-learning model that uses the comorbidity and medication data obtained from Taiwan's National Health Insurance Research Database to forecast the occurrence of CKD within the next 6 or 12 months before its onset, and hence its prevalence in the population. A total of 18,000 people with CKD and 72,000 people without CKD diagnosis were selected using propensity score matching. Their demographic, medication and comorbidity data from their respective two-year observation period were used to build a predictive model. Among the approaches investigated, the Convolutional Neural Networks (CNN) model performed best with a test set AUROC of 0.957 and 0.954 for the 6-month and 12-month predictions, respectively. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. The models can allow close monitoring of people at risk, early detection of CKD, better allocation of resources, and patient-centric management.
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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.