A non-parametric causal framework decomposes disparities in survival times into direct, indirect, and spurious pathway contributions using graphical models and the Causal Reduction Theorem.
Random survival forests
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An AI recommender system improves Cox Proportional Hazards model performance for predicting patient falls by suggesting 23 feature exclusions, 2 non-linear terms, and 221 interactions, raising C-index from 0.805 to 0.815.
QSurv uses Gauss-Legendre numerical quadrature and time-conditioned low-rank adaptation to enable scalable nonparametric continuous-time survival modeling with theoretical error bounds.
Transfer learning from PREDICT v3 and de-novo random survival forests improve calibration of five-year breast cancer survival predictions over the baseline in MA.27 data while handling missing information, with benefits seen in SEER but not TEAM validation.
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Causal Fairness for Survival Analysis
A non-parametric causal framework decomposes disparities in survival times into direct, indirect, and spurious pathway contributions using graphical models and the Causal Reduction Theorem.
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Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies
An AI recommender system improves Cox Proportional Hazards model performance for predicting patient falls by suggesting 23 feature exclusions, 2 non-linear terms, and 221 interactions, raising C-index from 0.805 to 0.815.
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A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature
QSurv uses Gauss-Legendre numerical quadrature and time-conditioned low-rank adaptation to enable scalable nonparametric continuous-time survival modeling with theoretical error bounds.
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Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer
Transfer learning from PREDICT v3 and de-novo random survival forests improve calibration of five-year breast cancer survival predictions over the baseline in MA.27 data while handling missing information, with benefits seen in SEER but not TEAM validation.