Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Decoding the renal-cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD.

Renal failure - 1 Dec 2026

Chen Ling, Wang Jing, Liu Guiqun, Zhao Yu, Zhou Zhu, Li Qing

Abstract excerpt

This study develops a dual-level machine learning framework for risk stratification and phenotyping of hearing loss (HL) in patients with chronic kidney disease (CKD) using data from the National Health and Nutrition Examination Survey (NHANES). From a cohort of 3,402 CKD patients, feature selection via univariate and multivariate logistic regression identified key predictors, which were used to construct...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Clustering Algorithms
Share to Hearing Loss

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Logistic Models

Share to Machine Learning

Share to Nutrition Surveys

Share to Predictive Learning Models

Share to Renal Insufficiency, Chronic

Share to Risk Assessment

Source

Open in PubMed Open full text

Identifiers

PMID: 42015590
DOI: 10.1080/0886022X.2026.2649658
PMCID: PMC13103990

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Decoding the renal-cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD.PMID 42015590Read ↗

Impact of Chronic Kidney Disease on the Risk of Hearing Loss: A Retrospective Cohort Study and Genetic Analysis.Semantic proximity Read ↗

Deep Learning Identifies Intelligible Predictors of Poor Prognosis in Chronic Kidney DiseaseSemantic proximity Read ↗

Interpretable Machine Learning Model for Pediatric Primary Nephrotic Syndrome Risk PredictionSemantic proximity Read ↗

Molecular Stratification of Chronic Kidney DiseaseSemantic proximity Read ↗

Accessible CKD Risk Index (ACRI): A Genetically Anchored, Mechanistically Validated, Income-Agnostic Early Detection Score for Chronic Kidney Disease - A Pan-Cohort Study Across Five Global Income SettingsSemantic proximity Read ↗

Early Prediction of Acute Kidney Injury in Pediatric Intensive Care Unit Patients Using an XGBoost-Based Machine Learning Model: A Retrospective StudySemantic proximity Read ↗

Predicting the risk of chronic kidney disease using Machine Learning AlgorithmsSemantic proximity Read ↗

Emerging technologies for early risk stratification and precision management of diabetic kidney disease: a multimodal framework integrating digital phenotypes and clinical biomarkers.Semantic proximity Read ↗

Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurementsSemantic proximity Read ↗

Development and Validation of a Dual-Track Machine Learning System for Cardiovascular Risk Reclassification: A Gatekeeper-Guided ApproachSemantic proximity Read ↗

Explainable Clinician-Supervised Artificial Intelligence as an Implementation Framework for Cardiovascular–Kidney–Metabolic Population Health: Synthetic Data Validation of the CHAPERONE-CKM FrameworkSemantic proximity Read ↗

Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.