10 citations · 16 across the 4 of their papers we have counts for
4 papers
Is this model reliable for everyone? Testing for strong calibration
Jean Feng, Alexej Gossmann, Romain Pirracchio +3
In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup. Such models are reliable across heterogeneous…
A Causal Roadmap for Generating High-Quality Real-World Evidence
Lauren E Dang, Susan Gruber, Hana Lee +23
Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks…
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions
Jean Feng, Alexej Gossmann, Gene Pennello +3
Performance monitoring of machine learning (ML)-based risk prediction models in healthcare is complicated by the issue of confounding medical interventions (CMI): when an algorithm…
Sequential algorithmic modification with test data reuse
Jean Feng, Gene Pennello, Nicholas Petrick +3
After initial release of a machine learning algorithm, the model can be fine-tuned by retraining on subsequently gathered data, adding newly discovered features, or more. Each modi…