PubMed
open access
CC BY-NC 4.0
Development and validation of an explainable machine learning model for predicting acute kidney injury in critically ill patients with primary peritonitis: a multicenter 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
- Early identification of acute kidney injury progression in critically ill patients with sepsis: interpretable machine learning approach.#440701
- Machine learning early risk assessment model for acute kidney injury in critically ill children: a retrospective cohort study.#399690
- Explainable machine learning using urinary metabolomics to predict pediatric sepsis-associated acute kidney injury: a two-center prospective observational study.#136113
- Supplementary file 3_Machine learning early risk assessment model for acute kidney injury in critically ill children: a retrospective cohort study.docx#350272
- Supplementary file 5_Machine learning early risk assessment model for acute kidney injury in critically ill children: a retrospective cohort study.docx#350274
Record · ID 316337
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