An autoregressive generative model trained on large-scale real-world patient data generates clinically plausible counterfactual trajectories that reproduce known patterns in COVID-19 simulations.
Evaluation of clinical prediction models (part 1): from development to external validation
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Generating Counterfactual Patient Timelines from Real-World Data
An autoregressive generative model trained on large-scale real-world patient data generates clinically plausible counterfactual trajectories that reproduce known patterns in COVID-19 simulations.