ClinicalTrials.gov
public full text
Public Domain
Machine Learning in the ICU: Predicting Mortality in Bloodstream Infections (ICU:Intensive Care Unit)
Using our own patient data, our study aimed to predict mortality that can develop in Carbapenem-resistant Gram-negative bacilli bloodstream infections with a machine learning-based model. In the intensive care unit, patients with bloodstream infections, both with and without mortality, will be examined retrospectively in two subgroups for comparison.
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
- Evaluation of Parameters Collected From Routine Data for the Diagnosis of Sepsis and Septic Shock and Their Influence on Time to Diagnosis and Patient Outcome#869492
- Artificial Intelligence for Sepsis Prediction in ICU#873467
- Benchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs#1122458
- Predicting Gram-negative bloodstream infection in elderly patients after isolation of GNB from non-blood specimens: a machine learning-based tool.#330752
- Prediction of Duration of Mechanical Ventilation in Acute Hypoxemic Respiratoty Failure#811823
Record · ID 855047
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