Aceso-DSAL: Discovering Clinical Evidences from Medical Literature Based on Distant Supervision and Active Learning.

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xiang Zhang, Jiaxin Hu, Qian Lu, Lu Niu, Xinqi Wang
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Abstract

Automatic extraction of valuable, structured evidence from the exponentially growing clinical trial literature can help physicians practice evidence-based medicine quickly and accurately. However, current research on evidence extraction has been limited by the lack of generalization ability on various clinical topics and the high cost of manual annotation. In this work, we address these challenges by constructing a PICO-based evidence dataset PICO-DS, covering five clinical topics. This dataset was automatically labeled by a distant supervision based on our proposed textual similarity algorithm called ROUGE-Hybrid. We then present an Aceso-DSAL model, an extension of our previous supervised evidence extraction model - Aceso. In Aceso-DSAL, distantly-labelled and multi-topic PICO-DS was exploited as training corpus, which greatly enhances the generalization of the extraction model. To mitigate the influence of noise unavoidably-introduced in distant supervision, we employ TextCNN and MW-Net models and a paradigm of active learning to weigh the value of each sample. We evaluate the effectiveness of our model on the PICO-DS dataset and find that it outperforms state-of-the-art studies in identifying evidential sentences.

Aceso-DSAL:基于远程监督和主动学习从医学文献中发现临床证据
从急剧增长的临床试验文献中自动提取有价值的结构化证据,有助于医生快速准确地实施循证医学。然而,由于缺乏对各种临床主题的概括能力以及人工标注的高成本,目前的证据提取研究一直受到限制。在这项工作中,我们通过构建一个基于 PICO 的证据数据集 PICO-DS,涵盖五个临床主题,来应对这些挑战。该数据集由我们提出的文本相似性算法 ROUGE-Hybrid 进行远距离监督自动标注。然后,我们提出了一个 Aceso-DSAL 模型,它是我们之前的监督证据提取模型 Aceso 的扩展。在Aceso-DSAL中,我们使用了远距离标签和多主题PICO-DS作为训练语料,这大大提高了提取模型的泛化能力。为了减轻远距离监督中不可避免地引入的噪声影响,我们采用了 TextCNN 和 MW-Net 模型以及主动学习范式来权衡每个样本的价值。我们在 PICO-DS 数据集上评估了我们模型的有效性,发现它在识别证据句子方面优于最先进的研究。
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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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