NCBI PubMed Central
open access
Development and Internal Validation of Machine Learning-Based Risk Prediction Models for Depression in Older Adults Undergoing Maintenance Hemodialysis.
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
- Comparison and validation of machine learning-based screening models for elevated depressive symptoms in peritoneal dialysis patients.#354480
- R code for "Heterogeneity in the Association between Iron Deficiency and Mortality Among Hemodialysis Patients: A Machine Learning Approach"#408478
- Multiple machine learning models for predicting major adverse cardiovascular events in dialysis with clinical and echocardiographic parameters: a retrospective cohort study.#362548
- Time-updated explainable machine learning predicts short-term mortality in peritoneal dialysis patients.#242230
- Time-updated explainable machine learning predicts short-term mortality in peritoneal dialysis patients.#240446
Record · ID 425040
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