NCBI PubMed Central
open access
Dynamic Individualized Prediction of Binary Clinical Outcomes Using Bayesian Generalized Linear Mixed Models With Application in Scleroderma.
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
- Kalman Filtering and Smoothing for Improving Precision in Horvitz--Thompson Estimation of Infectious Disease Prevalence#987086
- Modeling variable interactions using Bayesian networks to identify direct predictors of radiation-induced optic neuropathy after proton therapy: implications for personalized toxicity risk stratification#284309
- Bayesian Modeling of Bovine Tuberculosis Prevalence for Enhanced Estimation and Control in Intensive Dairy Farms: A Longitudinal Decision-Support Tools for Veterinarians and Policymakers.#1080553
- Target-Aware State-Adaptive $p$-Dirichlet Graph Neural Regression for Non-Invasive Body-Composition Estimation#790256
- Optimizing Tuberculosis Incidence Prediction: A Systematic Review of Hybrid Modeling Approaches and Machine Learning Techniques#1175103
Record · ID 440773
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