NCBI PubMed Central
open access
Time-updated explainable machine learning predicts short-term mortality in peritoneal dialysis patients.
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
- Time-updated explainable machine learning predicts short-term mortality in peritoneal dialysis patients.#240446
- Development and Validation of Machine Learning Models for Predicting Initiation of Emergency Dialysis in Advanced Chronic Kidney Disease#394544
- Early Prediction of Postoperative Urinary Retention After General Anesthesia Using Explainable Machine Learning.#1107495
- Multiple machine learning models for predicting major adverse cardiovascular events in dialysis with clinical and echocardiographic parameters: a retrospective cohort study.#362548
- Machine learning models of depressive symptoms in middle-aged and elderly patients with chronic kidney disease: A cross-sectional study based on CHARLS.#388633
Record · ID 242230
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