论文标题
DSR：评估分级疾病关系关系的集合
DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations
论文作者
论文摘要
在各种医学任务中，有效提取排名疾病 - 症状的关系是一个关键组成部分，包括计算机辅助的医学诊断或发现疾病之间意外关联的重要组成部分。虽然现有的疾病 - 症状关系提取方法被用作各种医疗任务的基础，但没有收集可以系统地评估此类方法的性能。在本文中，我们介绍了由五位受过全面训练的医生作为专家注释者创建的疾病 - 症状关系收集（DSR收集）。我们通过区分“症状”和“主要症状”来提供疾病的分级症状判断。此外，我们根据先前研究中使用的方法提供了几种强大的基准。第一种方法是基于单词嵌入的，第二种方法是基于医学文章中关键字的共发生的第二种方法。对于共发生方法，我们提出了一种适应性，其中不仅考虑了关键字，而且还考虑了医学文章的全文。对DSR收集的评估表明，根据NDCG，精度和召回率，提出的适应性的有效性。
The effective extraction of ranked disease-symptom relationships is a critical component in various medical tasks, including computer-assisted medical diagnosis or the discovery of unexpected associations between diseases. While existing disease-symptom relationship extraction methods are used as the foundation in the various medical tasks, no collection is available to systematically evaluate the performance of such methods. In this paper, we introduce the Disease-Symptom Relation collection (DSR-collection), created by five fully trained physicians as expert annotators. We provide graded symptom judgments for diseases by differentiating between "symptoms" and "primary symptoms". Further, we provide several strong baselines, based on the methods used in previous studies. The first method is based on word embeddings, and the second on co-occurrences of keywords in medical articles. For the co-occurrence method, we propose an adaption in which not only keywords are considered, but also the full text of medical articles. The evaluation on the DSR-collection shows the effectiveness of the proposed adaption in terms of nDCG, precision, and recall.
