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New HALO pipeline optimizes clinical data labeling under budget constraints

By PulseAugur Editorial · [1 sources] ·

Researchers have developed HALO, a novel pipeline for optimizing label acquisition from longitudinal clinical records within a limited budget. This framework jointly learns which evidence to select from patient histories and how to allocate different fidelity label sources. In a heart failure case study, HALO demonstrated superior performance in acquiring labels and training downstream classifiers compared to existing methods across various budget constraints. AI

IMPACT This research could improve the efficiency and effectiveness of training AI models on sensitive clinical data by optimizing the use of limited labeling resources.

RANK_REASON The cluster contains a research paper detailing a new methodology for data labeling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New HALO pipeline optimizes clinical data labeling under budget constraints

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The cluster contains a research paper detailing a new methodology for data labeling. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Huaiyuan Ying, Feiqing Huang, Ryumei Nakada, Katherine P. Liao, Tianxi Cai ·

    HALO: Hierarchical Evidence Selection and Reinforcement Learning for Allocating Budgeted Label Optimization from Longitudinal Clinical Records

    arXiv:2611.00986v1 Announce Type: new 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 …

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