Mar 5, 2026 · Artificial Intelligence

ML Predicts Dual Mortality Risk for HCC Liver Transplant Candidates (11,647 Cases)

Using a dataset of 11,647 hepatocellular carcinoma patients, a French research team combined ensemble learning, SHAP explainability, UMAP dimensionality reduction and K‑medoids clustering to build an interpretable model that outperforms traditional scores in predicting three‑month wait‑list mortality and defines seven clinically distinct risk sub‑groups.

Hepatocellular CarcinomaK-MedoidsLiver Transplantation

0 likes · 14 min read

ML Predicts Dual Mortality Risk for HCC Liver Transplant Candidates (11,647 Cases)

Xiaohongshu Tech REDtech

Jun 3, 2025 · Artificial Intelligence

Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient Evaluation

The TailoredBench framework dramatically reduces large‑language‑model evaluation cost and error by using a global probe set, model‑specific source selection, extensible K‑Medoids clustering, and calibration, achieving up to 300× speedup and a 31.4% MAE reduction across diverse benchmarks.

AI researchK-MedoidsLLM evaluation

0 likes · 10 min read

Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient Evaluation