机器学习减少健康科学系统性综述筛选工作量的可行性：一份工作文件
Viability of machine learning to reduce workload in systematic review screenings in the health sciences: a working paper
摘要：
系统评价是对某一特定主题的所有当前研究的总结和综合，是学术界的重要组成部分。它们在生物医学和健康科学中尤其重要，它们综合了医学证据的状态，并得出各种疾病，病理和治疗的最佳行动方案。由于存在大量文献以及研究的产出率，审查摘要可能是一个费力的过程。自动化可能会大大减少这种工作量。当然，由于书面语言的特殊性质，这种分类不容易自动化。机器学习也许能帮上忙。本文探讨了使用机器学习建模根据特定的排除/纳入标准对摘要进行分类的可行性和有效性，如在系统性综述的第一阶段所做的那样。具体任务是进行分类，以确定摘要是否为随机对照试验（RCT），这是医疗保健领域系统性综述中非常常见的分类。重复创建n=2042个标记摘要数据集的随机训练/测试分割（总共1000次），并在每个实例上训练和测试模型。使用贝叶斯分类器以及SVM分类器，并与非机器学习的文本分类的简单方法进行比较。SVM分类器被认为是非常有效的，产生了90%的准确率，以及0.84的F1得分，并产生了70%的潜在工作量减少。这表明，机器学习有可能显著改变医疗保健系统评价中的抽象筛选过程。
英文摘要：
Systematic reviews, which summarize and synthesize all the current research in a specific topic, are a crucial component to academia. They are especially important in the biomedical and health sciences, where they synthesize the state of medical evidence and conclude the best course of action for various diseases, pathologies, and treatments. Due to the immense amount of literature that exists, as well as the output rate of research, reviewing abstracts can be a laborious process. Automation may be able to significantly reduce this workload. Of course, such classifications are not easily automated due to the peculiar nature of written language. Machine learning may be able to help. This paper explored the viability and effectiveness of using machine learning modelling to classify abstracts according to specific exclusion/inclusion criteria, as would be done in the first stage of a systematic review. The specific task was performing the classification of deciding whether an abstract is a randomized control trial (RCT) or not, a very common classification made in systematic reviews in the healthcare field. Random training/testing splits of an n=2042 dataset of labelled abstracts were repeatedly created (1000 times in total), with a model trained and tested on each of these instances. A Bayes classifier as well as an SVM classifier were used, and compared to non-machine learning, simplistic approaches to textual classification. An SVM classifier was seen to be highly effective, yielding a 90% accuracy, as well as an F1 score of 0.84, and yielded a potential workload reduction of 70%. This shows that machine learning has the potential to significantly revolutionize the abstract screening process in healthcare systematic reviews.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
