PLOS
open access
CC BY 4.0
Predicting unplanned readmissions in neurological patients: A large-scale AI-driven analysis using CHAID decision trees
Background: Unplanned readmissions in neurology signify care gaps, increasing costs, and morbidity. This study aimed to develop and internally validate an AI model to predict unplanned readmissions among neurological patients in Jordan. Method and materials: We conducted a retrospective analysis of
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
- Predicting unplanned readmissions in neurological patients: A large-scale AI-driven analysis using CHAID decision trees.#1284643
- Artificial Intelligence Support for Stroke#826186
- Development and internal validation of an artificial intelligence-driven precision rehabilitation model for ischemic stroke: a retrospective cohort study.#1080593
- The continuous net benefit: assessing the clinical utility of prediction models when informing a continuum of decisions.#296527
- Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.#304977
Record · ID 1267836
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