aiqbench analysis
The paper proposes a reinforcement-learning pipeline that decides both which parts of longitudinal clinical records to read and which label source (free, silver, or gold) to acquire under a fixed budget. Its theory gives a finite-sample risk bound for a frozen policy, and its heart-failure experiments show gains on some budgets but not all, with the downstream classifier roughly tied at higher budgets. For ML readers, the main contribution is treating evidence selection as part of active learning rather than a fixed input, but the results come from one clinical case study and need replication.
HALO: Hierarchical Evidence Selection and Reinforcement Learning for Allocating Budgeted Label Optimization from Longitudinal Clinical Records
arXiv:2611.00986v1 Announce Type: cross Abstract: Learning from extensive longitudinal clinical records--spanning repeated encounters, evolving conditions, and heterogeneous documentation--under a finite labeling budget requires solving two coupled problems: deciding what evidence to keep and deciding which patients deserve higher-fidelity labels. Prior work on active learning and multi-fidelity label acquisition mainly addresses the second problem, while treating the evidence presented to the acquisition policy as fixed. We present HALO, a Hierarchical Evidence Selection and Reinforcement Learning pipeline for Allocating Budgeted Label Optimization from Longitudinal Clinical Records. This framework jointly learns what to read and what label source to acquire under budget. HALO first selects informative evidence from longitudinal records and then uses a budget-aware policy to allocate free-, silver-, and gold-fidelity label sources. For frozen policies, our theory characterizes asymmetric acquired-label channels and gives a budget-constrained finite-sample gold-risk bound for a common downstream logistic model. In a heart-failure case study, HALO attained the highest MedAlign acquired-label F1 at six of eight target budgets. A downstream classifier trained on HALO-acquired labels attained the highest external MGB F1 at budgets 15-60 and remained within 0.01 of the best comparator at budgets 75-120.
Abstract:Learning from extensive longitudinal clinical records--spanning repeated encounters, evolving conditions, and heterogeneous documentation--under a finite labeling budget requires solving two coupled problems: deciding what evidence to keep and deciding which patients deserve higher-fidelity labels. Prior work on active learning and multi-fidelity label acquisition mainly addresses the second problem, while treating the evidence presented to the acquisition policy as fixed. We present HALO, a Hierarchical Evidence Selection and Reinforcement Learning pipeline for Allocating Budgeted Label Optimization from Longitudinal Clinical Records. This framework jointly learns what to read and what label source to acquire under budget. HALO first selects informative evidence from longitudinal records and then uses a budget-aware policy to allocate free-, silver-, and gold-fidelity label sources. For frozen policies, our theory characterizes asymmetric acquired-label channels and gives a budget-constrained finite-sample gold-risk bound for a common downstream logistic model. In a heart-failure case study, HALO attained the highest MedAlign acquired-label F1 at six of eight target budgets. A downstream classifier trained on HALO-acquired labels attained the highest external MGB F1 at budgets 15-60 and remained within 0.01 of the best comparator at budgets 75-120.
Computation and Language (cs.CL); Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2611.00986 [cs.CL]
(or arXiv:2611.00986v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.00986 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 284 words
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