@Article{info:doi/10.2196/10281，作者=“Del Fiol, Guilherme and Michelson, Matthew and Iorio, Alfonso and Cotoi, Chris and Haynes, R Brian”，标题=“一种深度学习方法自动识别生物医学文献中科学严谨的临床研究报告:比较分析研究”，期刊=“J Med Internet Res”，年=“2018”，月=“6”，日=“25”，卷=“20”，号=“6”，页=“e10281”，关键词=“信息检索”;循证医学;深度学习;机器学习;背景:循证医学实践的一个主要障碍是在给定的临床主题上有效地找到科学合理的研究。目的:探讨一种从生物医学文献中检索科学合理的治疗研究的深度学习方法。方法:使用包含403216篇PubMed引文的噪声数据集，以标题和摘要为特征训练卷积神经网络。将深度学习模型与最先进的搜索过滤器进行比较，例如PubMed的临床查询广泛治疗过滤器，McMaster的文本搜索策略(无医学主题标题，MeSH，术语)和临床查询平衡治疗过滤器。先前注释的数据集(临床对冲)被用作金标准。结果:深度学习模型的召回率明显低于临床查询广义治疗过滤器(96.9{\%}vs 98.4% {\%}; P<.001); and equivalent recall to McMaster's textword search (96.9{\%} vs 97.1{\%}; P=.57) and Clinical Queries Balanced filter (96.9{\%} vs 97.0{\%}; P=.63). Deep learning obtained significantly higher precision than the Clinical Queries Broad filter (34.6{\%} vs 22.4{\%}; P<.001) and McMaster's textword search (34.6{\%} vs 11.8{\%}; P<.001), but was significantly lower than the Clinical Queries Balanced filter (34.6{\%} vs 40.9{\%}; P<.001). Conclusions: Deep learning performed well compared to state-of-the-art search filters, especially when citations were not indexed. Unlike previous machine learning approaches, the proposed deep learning model does not require feature engineering, or time-sensitive or proprietary features, such as MeSH terms and bibliometrics. Deep learning is a promising approach to identifying reports of scientifically rigorous clinical research. Further work is needed to optimize the deep learning model and to assess generalizability to other areas, such as diagnosis, etiology, and prognosis. ", issn="1438-8871", doi="10.2196/10281", url="//www.mybigtv.com/2018/6/e10281/", url="https://doi.org/10.2196/10281", url="http://www.ncbi.nlm.nih.gov/pubmed/29941415" }