基于较少数据的医学摘要句子分类研究

IF 5.9 Q1 Computer Science
Journal of Healthcare Informatics Research Pub Date : 2023-08-30 eCollection Date: 2023-12-01 DOI:10.1007/s41666-023-00141-6
Yan Hu, Yong Chen, Hua Xu
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引用次数: 0

摘要

随着生物医学出版物的空前增长,在书目数据库(即PubMed)中提供结构化摘要非常重要,从而有助于研究人员根据需要进行信息检索和知识合成。在这里,我们提出了一种基于快速学习的方法,将随机临床试验(RCT)和观察性研究(OS)的医学摘要中的句子分类到引言、背景、方法、结果和结论的子部分,使用现有的RCT语料库(PubMed 200k/20k RCT)以及新建的OS语料库(PubMed 20k OS)。测试了4个BERT模型变体组合中的5个手动设计的模板,并将其与以前的分层顺序标记网络架构和传统的基于BERT的句子分类方法进行了比较。在PubMed 200k和20k RCT数据集上,我们的F1总分分别为0.9508和0.9401。在很少的击球设置下,我们证明只有20%的训练数据足以通过HSLN模型获得可比的F1分数(我们的0.9266和HSLN的0.9263)。当在RCT数据集上训练时,我们的方法在OS数据集上获得了0.9065的F1分数。当在OS数据集上训练时,我们的方法在RCT数据集上获得了0.9203的F1分数。我们表明,即使使用较少的训练样本,基于即时学习的方法也优于现有方法。此外,与现有方法相比,所提出的方法在两种类型的医学出版物中表现出更好的可推广性。我们在以下网站公开数据集和代码:https://github.com/YanHu-or-SawyerHu/prompt-learning-based-sentence-classifier-in-medical-abstracts.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards More Generalizable and Accurate Sentence Classification in Medical Abstracts with Less Data.

With the unprecedented growth of biomedical publications, it is important to have structured abstracts in bibliographic databases (i.e., PubMed), thus, to facilitate the information retrieval and knowledge synthesis in needs of researchers. Here, we propose a few-shot prompt learning-based approach to classify sentences in medical abstracts of randomized clinical trials (RCT) and observational studies (OS) to subsections of Introduction, Background, Methods, Results, and Conclusion, using an existing corpus of RCT (PubMed 200k/20k RCT) and a newly built corpus of OS (PubMed 20k OS). Five manually designed templates in a combination of 4 BERT model variants were tested and compared to a previous hierarchical sequential labeling network architecture and traditional BERT-based sentence classification method. On the PubMed 200k and 20k RCT datasets, we achieved overall F1 scores of 0.9508 and 0.9401, respectively. Under few-shot settings, we demonstrated that only 20% of training data is sufficient to achieve a comparable F1 score by the HSLN model (0.9266 by us and 0.9263 by HSLN). When trained on the RCT dataset, our method achieved a 0.9065 F1 score on the OS dataset. When trained on the OS dataset, our method achieved a 0.9203 F1 score on the RCT dataset. We show that the prompt learning-based method outperformed the existing method, even when fewer training samples were used. Moreover, the proposed method shows better generalizability across two types of medical publications when compared with the existing approach. We make the datasets and codes publicly available at: https://github.com/YanHu-or-SawyerHu/prompt-learning-based-sentence-classifier-in-medical-abstracts.

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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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