AutoAdapt：临床分布偏移下的可靠小样本适应
AutoAdapt: Reliable Few-Shot Adaptation under Clinical Distribution Shifts
浏览论文内容
中文总结 AI 辅助
本研究针对临床分布偏移下的小样本适应问题，提出AutoAdapt框架，通过Adapter与Automator设计及可靠性规则实现高效适配，在多类临床数据集上仅用少量患者即可达到最先进性能。
中文摘要 AI 辅助
大型预训练临床模型为通过将模型适配至不同医院来复用跨医院学习到的先验知识提供了实用途径。在实际应用中，目标医院可能仅拥有少量标注患者队列，这一设置通常被称为小样本适应。该过程需要做出多项决策，例如适配哪一个预训练模型、更新模型的多少部分、使用哪些患者。然而，这一过程面临两大核心挑战：其一，最佳适配策略因临床任务而异；其二，由于患者队列规模小，对候选策略的评估与比较变得不可靠。本研究提出AutoAdapt，包含两项核心设计以应对上述挑战：Adapter定义了可扩展的适配方案空间，Automator则基于适配患者中的证据形成加权方案组合。我们提出一项可靠性规则，以确保仅选择在大多数可用患者上最有效的策略，随后将这些选定策略组合起来实现有效的小样本适应。我们在重症监护、急诊和诊断数据集上开展了大量实验，结果表明，AutoAdapt仅使用少量患者进行适配，即可始终达到最先进的性能。
英文摘要
Large pretrained clinical models provide a practical way to reuse learned prior knowledge across hospitals by adapting models to them. In practice, a target hospital may only have a small labeled patient cohort, a setting commonly referred to as few-shot adaptation. This requires making multiple decisions, such as which pretrained model to adapt, how much of the model to update, and which patients to use. Nevertheless, this process faces two primary challenges. First, the best adaptation strategy varies across clinical tasks. Second, evaluating and comparing candidate strategies becomes unreliable due to the small patient cohort. In this work, we introduce AutoAdapt with two core designs to deal with these challenges. The Adapter defines an extensible space of adaptation recipes, and the Automator forms a weighted recipe combination from evidence within the adaptation patients. We propose a reliability rule to ensure that only the most effective strategy on most available patients will be selected. These selected strategies then form a combination for effective few-shot adaptation. We conduct extensive experiments across critical care, emergency care, and diagnostic datasets, and the results show that AutoAdapt consistently achieves state-of-the-art performance using only a few patients for adaptation.
