Gaussian-Process Borrowing for a Patient-Specific Choice
Abstract
Contextual Gaussian-process selection can support individualized treatment comparison, but sparse profiles may depend heavily on borrowed precision. Validation should target profiles where the preferred action changes and record which variables are available before action. Numerical simulated preferences remain separate from observed treatment benefit, requiring an independent evidence design at the relevant patient level. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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