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ARXIV:2604.11165 · CLINICAL DECISION SUPPORT · SUBMITTED 14 APR · 16:51 UTC · FRESHNESS STALE
ARXIV:2604.11165CLINICAL DECISION SUPPORTSUBMITTED 14 APR · 16:51 UTCFRESHNESS STALEDoudou Zhou · Yiran Zhang · Dian Jin · Yingye Zheng · Lu Tian · Tianxi Cai · arXiv
This paper introduces a doubly robust Q-learning framework for learning cost-optimal sequential testing policies from retrospective data in clinical decision-making.
Opportunity summary
Pain This paper introduces a doubly robust Q-learning framework for learning cost-optimal sequential testing policies from retrospective data in clinical decision-making.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
This paper introduces a doubly robust Q-learning framework for learning cost-optimal sequential testing policies from retrospective data in clinical decision-making. We study the problem of learning cost-optimal sequential decision policies from retrospective data, where…
Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies for what to measure and when to stop ascertaining. We study the problem of learning cost-optimal sequential decision…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We study the problem of learning cost-optimal sequential decision policies from retrospective data, where test availability depends on prior results, inducing informative missingness. Code…
Clinical Decision Support moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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This paper introduces a doubly robust Q-learning framework for learning cost-optimal sequential testing policies from retrospective data in clinical decision-making.
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10.48550/arXiv.2604.11165This paper introduces a doubly robust Q-learning framework for learning cost-optimal sequential testing policies from retrospective data in clinical decision-making.
Abstract
Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies for what to measure and when to stop ascertaining. We study the problem of learning cost-optimal sequential decision policies from retrospective data, where test availability depends on prior results, inducing informative missingness. Under a sequential missing-at-random mechanism, we develop a doubly robust Q-learning framework for estimating optimal policies. The method introduces path-specific inverse probability weights that account for heterogeneous test trajectories and satisfy a normalization property conditional on the observed history. By combining these weights with auxiliary contrast models, we construct orthogonal pseudo-outcomes that enable unbiased policy learning when either the acquisition model or the contrast model is correctly specified. We establish oracle inequalities for the stage-wise contrast estimators, along with convergence rates, regret bounds, and misclassification rates for the learned policy. Simulations demonstrate improved cost-adjusted performance over weighted and complete-case baselines, and an application to a prostate cancer cohort study illustrates how the method reduces testing cost without compromising predictive accuracy.
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PROBLEM
This paper introduces a doubly robust Q-learning framework for learning cost-optimal sequential testing policies from retrospective data in clinical decision-making. We study the problem of learning cost-optimal sequential decision policies from retrospective data, where test av...
METHOD
Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies for what to measure and when to stop ascertaining. We study the problem of learning cost-optimal sequential decision policies fr...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We study the problem of learning cost-optimal sequential decision policies from retrospective data, where test availability depends on prior results, inducing informative missingness. Code availability is...
WHY NOW
Clinical Decision Support moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
This paper introduces a doubly robust Q-learning framework for learning cost-optimal sequential testing policies from retrospective data in clinical decision-making. We study the problem of learning cost-optimal sequential decision policies from retrospective data, where test availability depends on prior results, inducing informative missingness.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies for what to measure and when to stop ascertaining. We study the problem of learning cost-optimal sequential decision policies from retrospective data, where test availability depends on prior results, inducing informative missingness.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We study the problem of learning cost-optimal sequential decision policies from retrospective data, where test availability depends on prior results, inducing informative missingness. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Clinical Decision Support moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This paper introduces a doubly robust Q-learning framework for learning cost-optimal sequential testing policies from retrospective data in clinical decision-making.
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