通过资格调整实现协议优化：POET
POET: Protocol Optimization via Eligibility Tuning
AI总结：
POET通过引入可解释的语义轴，为临床试验设计提供了一种实用且可解释的AI辅助生成资格标准的方法。
摘要：
资格标准（EC）对于临床试验设计至关重要，但撰写这些标准仍然是临床医生耗时且具有认知负担的任务。现有的自动化方法往往处于两个极端：要么需要高度结构化的输入，如预定义的实体以生成特定的标准，要么依赖于端到端系统，从最小的输入如试验描述中生成完整的资格标准，限制了其实际应用价值。在本工作中，我们提出了一种引导生成框架，引入了可解释的语义轴，如人口统计学、实验室参数和行为因素，以引导EC生成。这些轴通过大型语言模型衍生，提供了在特异性和实用性之间的中间地带，使临床医生能够在不指定确切实体的情况下引导生成。此外，我们还提出了一种可重用的基于规则的评估框架，用于评估生成的标准在临床有意义的维度上。我们的结果表明，我们的引导生成方法在自动、基于规则的和临床评估中均一致优于无引导生成，为AI辅助试验设计提供了一种实用且可解释的解决方案。
英文摘要：
Eligibility criteria (EC) are essential for clinical trial design, yet drafting them remains a time-intensive and cognitively demanding task for clinicians. Existing automated approaches often fall at two extremes either requiring highly structured inputs, such as predefined entities to generate specific criteria, or relying on end-to-end systems that produce full eligibility criteria from minimal input such as trial descriptions limiting their practical utility. In this work, we propose a guided generation framework that introduces interpretable semantic axes, such as Demographics, Laboratory Parameters, and Behavioral Factors, to steer EC generation. These axes, derived using large language models, offer a middle ground between specificity and usability, enabling clinicians to guide generation without specifying exact entities. In addition, we present a reusable rubric-based evaluation framework that assesses generated criteria along clinically meaningful dimensions. Our results show that our guided generation approach consistently outperforms unguided generation in both automatic, rubric-based and clinician evaluations, offering a practical and interpretable solution for AI-assisted trial design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
