Scalable Random Survival Forests via Risk Set Sampling

Machine Learning with Applications

A Repeated-Evaluation Comparison of Traditional, Machine Learning, and Deep Learning Survival Models Across Static and Longitudinal Data

Traditional survival models were more accurate and stable for datasets containing only static baseline covariates, whereas the LSTM-based model showed higher discrimination on longitudinal survival data with genuine temporal structure, though this advantage is confounded with greater access to patient history and canno...

survkl: an R package for transfer-learning-based integrated Cox models

The survkl software is introduced, which enables the incorporation of external summary-level information with newly collected time-to-event data to support more robust and accurate predictions in survival analysis.

Cancer Survival Prediction through Multifactorial Analysis with Machine Learning

These results validate the feasibility of applying artificial intelligence to Brazilian hospital oncology records, demonstrating the potential of these models for future clinical decision-support applications within the Unified Health System (SUS).

Cross-Cohort Reliability of Machine Learning for Breast Cancer Survival Prediction

Transportability limitations can affect discrimination, probability calibration, and uncertainty estimates even when machine-learning pipelines use the same input variables. This study evaluated 5-year breast cancer survival models across METABRIC and TCGA-BRCA using age and ER, PR, and HER2 status. Logistic regression...

Lung cancer risk prediction using interpretable ensemble models on lifestyle and clinical data

The results suggest that stacking-based ensembles can improve risk prediction from lifestyle and clinical indicators while maintaining model transparency through SHAP and LIME explanations, highlighting the potential of interpretable ensemble learning as a decision-support tool for lung cancer risk assessment.