NCBI PubMed Central
open access
Machine learning-based prediction of bloodstream infection in rectal carbapenem-resistant Enterobacterales carriers: a multicenter retrospective cohort study.
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
- Machine Learning in the ICU: Predicting Mortality in Bloodstream Infections (ICU:Intensive Care Unit)#855047
- Table 1_Molecular epidemiology and clinical profiles of carbapenem-resistant Enterobacterales in neonates from two large children’s hospitals in southwestern China.docx#210824
- Table 2_Molecular epidemiology and clinical profiles of carbapenem-resistant Enterobacterales in neonates from two large children’s hospitals in southwestern China.docx#210825
- Molecular Characterization of Carbapenemase-Producing Enterobacterales#834688
- Piperacillin-tazobactam and Temocillin as Carbapenem-alternatives for the Treatment of Severe Infections Due to Extended-spectrum Beta-lactamase-Producing Gram-negative Enterobacteriaceae in the Intensive Care Unit#349980
Record · ID 476883
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