Abstract
While dense biomedical embeddings achieve strong performance, their black-box nature limits their utility in clinical decision-making. Recent question-based interpretable embeddings represent text as binary answers to natural-language questions, but these approaches often rely on heuristic or surface-level contrastive signals and overlook specialized domain knowledge. We propose QIME, an ontology-grounded framework for constructing interpretable medical text embeddings in which each dimension corresponds to a clinically meaningful yes/no question. By conditioning on cluster-specific medical concept signatures, QIME generates semantically atomic questions that capture fine-grained distinctions in biomedical text. Furthermore, QIME supports a training-free embedding construction strategy that eliminates per-question classifier training while further improving performance. Experiments across biomedical semantic similarity, clustering, and retrieval benchmarks show that QIME consistently outperforms prior interpretable embedding methods and substantially narrows the gap to strong black-box biomedical encoders, while providing concise and clinically informative explanations.
Abstract (translated)
虽然密集的生物医学嵌入能够取得优异的表现，但它们黑箱性质限制了其在临床决策中的实用性。最近基于问题的方法生成可解释的嵌入，将文本表示为对自然语言问题的回答形式（即二进制答案），然而这些方法往往依赖于启发式或表面对比信号，并且忽视了专门领域的知识。我们提出了QIME框架，这是一种以本体论为基础的构建可解释医学文本嵌入的方法，在这种方法中每个维度对应一个临床有意义的是/否问题。通过依据聚类特有的医学概念标识进行条件设定，QIME生成语义上原子的问题，捕捉生物医学文本中的细微差别。此外，QIME支持一种无需训练的嵌入构建策略，该策略消除了对每个问题分类器的单独训练需求，并进一步提升了性能。跨生物医学语义相似度、聚类和检索基准实验显示，QIME在可解释性嵌入方法中始终表现出色，大幅缩小了与强大黑箱生物医学编码器之间的差距，并提供了简洁且具有临床信息价值的解释。
URL
https://arxiv.org/abs/2603.01690
