arXiv (All)
open access
The Entropy Triangle Method (ETM): A novel framework for the prevention of cardiac arrhythmia with a review of more than 10,000 patients
One of the most important problems in medicine is to facilitate prediction. In this study, we propose entropy triangle method, a novel framework for predicting heart rhythms using a novel machine learning technique. This framework includes three steps: feature engineering, entropy triangle oversampl
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- An interpretable machine learning model for predicting 1-year major adverse cardiovascular events in patients with type 2 diabetes and hypertension.#438973
- A Comparison of Machine Learning Algorithms for Predicting Hypertension Incidence Based on Cohort Study.#174525
- Antihypertensive Combinations Modify Cardiovascular Risk Factor Importance: A Machine Learning Analysis of the ACCOMPLISH Trial.#189964
- Machine Learning for Cardiovascular Prevention Prescriptions: Real-World vs. Synthetic Data#364034
- Development and temporal validation of a machine learning-based prediction model for long-term depressive symptoms in older adults with cardiovascular disease or hypertension: A longitudinal cohort study.#1054328
Record · ID 1077116
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive