Machine Learning Prediction of Progression to Dialysis in Patients With Polycystic Kidney Disease: Population-Based Retrospective Cohort Study

ML models can predict dialysis risk in patients with ADPKD using administrative data with temporal validation. This approach may support risk stratification by helping identify individuals at higher predicted risk who may warrant closer monitoring and further specialist evaluation.

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Publication Details

Journal
JMIR Medical Informatics
Published
2026-03-16
DOI
https://doi.org/10.2196/80343
Primary Topic
Genetic and Kidney Cyst Diseases
Type
article
Field-Weighted Citation Impact
0.00
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article

Machine Learning Prediction of Progression to Dialysis in Patients With Polycystic Kidney Disease: Population-Based Retrospective Cohort Study

Hung‐Hsiang Liou, Ben Chang Shia, Mingchih Chen, Cheng-Hao Chang et al.
JMIR Medical Informatics
Genetic and Kidney Cyst Diseases
article

Machine Learning Prediction of Progression to Dialysis in Patients With Polycystic Kidney Disease: Population-Based Retrospective Cohort Study

Hung‐Hsiang Liou, Ben Chang Shia, Mingchih Chen, Cheng-Hao Chang, Ming-Hsien Tsai, Yu-Wei Fang, Chingying Liang, Yen-Chun Huang
article en

Abstract

ML models can predict dialysis risk in patients with ADPKD using administrative data with temporal validation. This approach may support risk stratification by helping identify individuals at higher predicted risk who may warrant closer monitoring and further specialist evaluation.

JMIR Medical InformaticsVol. 14
Tamkang University (TW), Fu Jen Catholic University (TW), Jen-Ai Hospital (TW), Memorial Hospital (US)
Openalex Percentile: Top 21%
Genetic and Kidney Cyst Diseases
DOIOpen Access PDFOpenAlex
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Machine Learning Prediction of Progression to Dialysis in Patients With Polycystic Kidney Disease: Population-Based Retrospective Cohort Study — Hung‐Hsiang Liou, Ben Chang Shia, et al. · JMIR Medical Informatics (2026) | TGRS Research Map | TGRS