通过标签化方法解释炎症预测模型中的群体特征
Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation
- 基于局部特征重要性识别数据中的相似群体
- 生成的标签解释与营养学领域知识一致
- 适合关注模型可解释性的医疗AI研究者
机器学习正在革新营养科学，使系统能够从数据中学习并做出智能决策。然而，这些模型的复杂性常导致其决策过程难以理解，亟需可解释性技术以增强信任和透明度。一种未被充分探索的解释方式是群体解释，它针对具有相似特征的一组实例提供解释。与聚焦个体或全局模型行为的传统方法不同，群体可解释性在中间粒度上提供了独特见解。我们提出一种新框架，基于局部特征重要性分数在数据集中识别群体，并通过标签生成简洁的集群描述。我们在基于食物的炎症预测模型上评估了该框架，结果表明其能生成与领域知识相符的可靠解释。
原文摘要 · Abstract (English)
Machine learning is revolutionizing nutrition science by enabling systems to learn from data and make intelligent decisions. However, the complexity of these models often leads to challenges in understanding their decision-making processes, necessitating the development of explainability techniques to foster trust and increase model transparency. An under-explored type of explanation is cohort explanation, which provides explanations to groups of instances with similar characteristics. Unlike traditional methods that focus on individual explanations or global model behavior, cohort explainability bridges the gap by providing unique insights at an intermediate granularity. We propose a novel framework for identifying cohorts within a dataset based on local feature importance scores, aiming to generate concise descriptions of the clusters via tags. We evaluate our framework on a food-based inflammation prediction model and demonstrated that the framework can generate reliable explanations that match domain knowledge.
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