arXiv (All)
open access
Improving Fairness of Large Language Model-Based ICU Mortality Prediction via Case-Based Prompting
Accurately predicting mortality risk in intensive care unit (ICU) patients is critical for clinical decision-making. Large language models (LLMs) are increasingly explored for clinical prediction using structured medical data, but their outputs may exhibit demographic disparities. Mitigating such di
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
- Large language model driven multicenter prediction and explainable risk attribution of acute kidney injury.#469358
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.#269516
- Large language models in medical time series analysis#1036945
- Generalizing intensive care AI across time scales in resource-limited settings#221594
- Deep learning-based in-hospital mortality prediction using long-term sequential data in ICU patients: a multi-center validation study.#618161
Record · ID 1143654
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