NCBI PubMed Central
open access
Machine Learning Model to Predict Postmastectomy Breast Reconstruction Complications.
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.
Checking your browser - reCAPTCHA Checking your browser before accessing pmc.ncbi.nlm.nih.gov ... Click here if you are not automatically redirected after 5 seconds.
Related documents
- Explainable machine learning for breast cancer prediction in resource-constrained settings: A multi-algorithmic framework integrating shap-based transparency with clinical decision support#784464
- Interpretable machine learning for prognostic prediction in young women with triple-negative invasive ductal breast cancer: a Boruta-SHAP integrated approach.#280920
- Artificial Intelligence-predicted Outcomes of Breast Reconstruction: Progress, Pitfalls, and Path Forward.#636311
- Integrating Serum Lipid Biomarkers Into Machine Learning for the Differential Diagnosis of Breast Nodules.#479257
- Interpretability of machine learning‐based prediction models in healthcare#335863
Record · ID 25586 · SHA-256 beb204b495da8be5
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