Back to search

Article

Development and Validation of a Dual-Track Machine Learning System for Cardiovascular Risk Reclassification: A Gatekeeper-Guided Approach

2026-06-11

Abstract excerpt

<title>Abstract</title> <p>Background Occult diabetic kidney disease (ODKD), defined as elevated albuminuria in the presence of a normal estimated glomerular filtration rate, represents an important clinical blind spot. Conventional single-track models are limited by phenotypic heterogeneity. Cardiovascular disease (CVD) risk is therefore often overestimated, and over-medicalization in primary care may be promot...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
b919724f-0a75-59c3-80f1-49a4d4df741b
DOI
10.21203/rs.3.rs-9824399/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Development and Validation of a Dual-Track Machine Learning System for Cardiovascular Risk Reclassification: A Gatekeeper-Guided ApproachDOI 10.21203/rs.3.rs-9824399/v1
Multi-Dataset Diabetes Prediction Using Stacking Ensemble Learning and Explainable Artificial IntelligenceSemantic proximity
An Interpretable Machine Learning Model for the Diagnosis of Coronary Heart Disease in Patients with Type 2 Diabetes Mellitus: A Multi-Center Cohort StudySemantic proximity
Explainable Hybrid CNN SVM Framework for Cardiovascular Disease Risk Prediction Using Clinical DataSemantic proximity
A Web-Based Clinical Decision Support System for Cardio-Kidney Risk Stratification in Critically Ill Patients with Diabetic Kidney Disease: Development, SHAP Interpretability, and Multi-Center External ValidationSemantic proximity
Internal and External Validation of Machine Learning Algorithms Versus FINDRISC for Incident Type 2 Diabetes: A Transparent, Explainable Benchmark Using SHAPSemantic proximity
Refined selection of individuals for preventive cardiovascular disease treatment with a Transformer-based risk modelSemantic proximity
A Community-Based Machine Learning Model for Predicting Major Adverse Cardiovascular Events in Kazakh Hypertensive PatientsSemantic proximity
Machine Learning–Derived Cardiometabolic Risk Burden Identified from Health Checkup Data in a Japanese PopulationSemantic proximity
Comprehensive, Transparent, and Fair Machine Learning Models for Hypertension Risk Prediction: Benchmarking With Framingham, External Validation, Individual-Level Analysis, and Equitable Clinical UtilitySemantic proximity
Evaluating Feature Selection Methods and Feature Contributions for Cardiovascular Disease Risk PredictionSemantic proximity
Comparative Performance of Linear Regression and Machine Learning Models for Predicting Glycemic Status in Uncontrolled Type 2 Diabetes: SHAP-Based AnalysisSemantic proximity
Development of Explainable Machine Learning Framework for Early Detection and Risk Stratification of Diabetes in Age Specific VariationsSemantic proximity
Select a neighboring publication to make it the new centre.