Doubly Robust Q-Learning Cuts Clinical Testing Costs
Researchers have developed a doubly robust Q-learning framework for learning cost-optimal sequential testing policies from retrospective clinical data, addressing the challenge of selecting which tests to order and when to stop given that test availability depends on prior results. The method handles informative missingness through path-specific inverse probability weights and auxiliary contrast models, enabling unbiased policy learning when either the acquisition or contrast model is correctly specified. Simulations and a prostate cancer cohort application show the approach reduces testing costs without sacrificing predictive accuracy compared to weighted and complete-case baselines.
Researchers have developed a doubly robust Q-learning framework for learning cost-optimal sequential testing policies from retrospective clinical data, addressing the challenge of selecting which tests to order and when to stop given that test availability depends on prior results. The method handles informative missingness through path-specific inverse probability weights and auxiliary contrast models, enabling unbiased policy learning when either the acquisition or contrast model is correctly specified. Simulations and a prostate cancer cohort application show the approach reduces testing costs without sacrificing predictive accuracy compared to weighted and complete-case baselines.
- New doubly robust Q-learning framework optimizes sequential clinical testing decisions from retrospective data where test availability depends on prior results
- Method uses path-specific inverse probability weights and orthogonal pseudo-outcomes to handle informative missingness and enable unbiased policy learning
- Theoretical guarantees include oracle inequalities, convergence rates, regret bounds, and misclassification rates for the learned policy
- Empirical validation on prostate cancer cohort demonstrates cost reduction without compromising diagnostic accuracy versus standard approaches
This work addresses a fundamental challenge in clinical AI: learning optimal decision policies from real-world data where missingness is not random but driven by prior test results. The doubly robust framework provides theoretical guarantees and practical robustness when model assumptions are violated, which is critical for high-stakes medical applications where both cost and accuracy matter.
- Doubly robust methods can handle realistic clinical data where missingness is informative, expanding the applicability of offline policy learning beyond idealized assumptions
- The framework supports heterogeneous test trajectories and adaptive stopping rules, enabling personalized testing strategies rather than one-size-fits-all protocols
- Theoretical guarantees on convergence and regret provide confidence for deployment in regulated medical settings where empirical validation alone may be insufficient
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