论文标题
从语料库级统计数据合理化医疗关系预测
Rationalizing Medical Relation Prediction from Corpus-level Statistics
论文作者
论文摘要
如今，机器学习模型的可解释性变得越来越重要，尤其是在医疗领域。为了阐明如何合理化医疗关系预测，我们提出了一个新的可解释框架，该框架受到有关人类记忆如何工作的现有理论的启发，例如回忆和认可的理论。鉴于语料库级统计数据，即临床文本语料库的全局共发生图，以预测两个实体之间的关系，我们首先回忆起与目标实体相关的丰富背景，然后识别这些上下文之间的关系相互作用以形成模型理性，这将有助于最终预测。我们在现实世界中的公共临床数据集上进行实验，并表明我们的框架不仅可以在神经基线模型的全面列表中实现竞争性预测性能，而且还提出了证明其预测合理性的理由。我们进一步与医学专家进行了深入的合作，以验证模型原理对临床决策的有用性。
Nowadays, the interpretability of machine learning models is becoming increasingly important, especially in the medical domain. Aiming to shed some light on how to rationalize medical relation prediction, we present a new interpretable framework inspired by existing theories on how human memory works, e.g., theories of recall and recognition. Given the corpus-level statistics, i.e., a global co-occurrence graph of a clinical text corpus, to predict the relations between two entities, we first recall rich contexts associated with the target entities, and then recognize relational interactions between these contexts to form model rationales, which will contribute to the final prediction. We conduct experiments on a real-world public clinical dataset and show that our framework can not only achieve competitive predictive performance against a comprehensive list of neural baseline models, but also present rationales to justify its prediction. We further collaborate with medical experts deeply to verify the usefulness of our model rationales for clinical decision making.
