In the latest years, the healthcare domain has seen an increasing interest in the definition of intelligent systems to support clinicians in their everyday tasks and activities. Among others, also the field of Evidence-Based Medicine is impacted by this twist, with the aim to combine the reasoning frameworks proposed thus far in the field with mining algorithms to extract structured information from clinical trials, clinical guidelines, and Electronic Health Records. In this paper, we go beyond the state of the art by proposing a new end-to-end pipeline to address argumentative outcome analysis on clinical trials. More precisely, our pipeline is composed of (i) an Argument Mining module to extract and classify argumentative components (i.e., evidence and claims of the trial) and their relations (i.e., support, attack), and (ii) an outcome analysis module to identify and classify the effects (i.e., improved, increased, decreased, no difference, no occurrence) of an intervention on the outcome of the trial, based on PICO elements. We annotated a dataset composed of more than 500 abstracts of Randomized Controlled Trials (RCT) from the MEDLINE database, leading to a labeled dataset with 4198 argument components, 2601 argument relations, and 3351 outcomes on five different diseases (i.e., neoplasm, glaucoma, hepatitis, diabetes, hypertension). We experiment with deep bidirectional transformers in combination with different neural architectures (i.e., LSTM, GRU and CRF) and obtain a macro F1-score of.87 for component detection and.68 for relation prediction, outperforming current state-of-the-art end-to-end Argument Mining systems, and a macro F1-score of.80 for outcome classification.

近年来，医疗保健领域对智能的定义越来越感兴趣。系统来支持临床医生的日常工作和活动。其中，循证医学领域也受到这种扭曲的影响，目的是将迄今为止在该领域提出的推理框架与挖掘算法相结合，以从临床试验、临床指南和电子健康记录中提取结构化信息。在本文中，我们通过提出一种新的端到端管道来解决临床试验的争论结果分析，从而超越了现有技术。更准确地说，我们的管道由（i）一个论证挖掘模块组成，用于提取和分类论证成分（即审判的证据和主张）及其关系（即支持、攻击），以及（ii）结果分析模块识别和分类效果（即改善、增加、减少、无差异、没有发生）对试验结果的干预，基于 PICO 元素。我们注释了一个由 MEDLINE 数据库中的 500 多个随机对照试验 (RCT) 摘要组成的数据集，从而产生了一个带有 4198 个参数组件、2601 个参数关系和 3351 个关于五种不同疾病的结果的标记数据集（即，肿瘤、青光眼、肝炎、糖尿病、高血压）。我们将深度双向变换器与不同的神经架构（即 LSTM、GRU 和 CRF）结合进行实验，并获得了用于组件检测的宏 F1 分数 87 和用于关系预测的 68，优于当前最先进的技术端到端参数挖掘系统，以及用于结果分类的宏观 F1 分数 0.80。