IRIS
metadata only
Machine learning prediction of mechanical dilatation in transvenous lead extraction for cardiac device-related infections: insights from a high-volume centre
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
- Development and external validation of a machine learning model for predicting severe immediate postoperative complications in adults undergoing cardiac surgery.#242269
- Additional file 2 of Scoping review of methodology for aiding generalisability and transportability of clinical prediction models#1145925
- Risk prediction of postoperative acute kidney injury in patients undergoing off-pump coronary artery bypass grafting based on machine learning.#425133
- An explainable machine learning model for predicting in-hospital infection in patients with systemic lupus erythematosus.#437399
- Machine learning for predicting clinical outcomes in emergency department patients with acute respiratory infections: A scoping review#162255
Record · ID 350939
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