支持基于证据的医学的自然语言处理:一个范围审查
Zihan Xu1, Haotian Ma1, Gongbo Zhang2
1Weill Cornell Medicine.
概括
自然语言处理 (NLP) 方法对于基于证据的医学 (EBM) 是至关重要的,因为研究的压倒性量. 这次审查突出了NLP的重点.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 基于证据的医学 (EBM) 在临床决策中依赖最好的科学证据.
- 医学文献的快速增长需要先进的证据管理方法.
- 自然语言处理 (NLP) 为处理大规模医疗数据提供了潜在的解决方案.
研究的目的:
- 在EBM中对NLP应用进行全面的调查.
- 系统地审查NLP在EBM的五个核心步骤中的作用:询问,获取,评估,应用和评估.
- 确定当前的局限性,并提出EBM中NLP的未来研究方向.
主要方法:
- 对EBM的NLP进行了129项研究研究的系统审查.
- 对NLP技术的分析,以支持EBM流程的每个阶段.
- 识别研究缺口和未来的机会.
主要成果:
- 在EBM中,NLP在加强临床决策方面发挥着关键作用.
- 在EBM的五个基本步骤中应用NLP方法.
- 该评论详细介绍了NLP对证据提取,合成,评估和总结的影响.
结论:
- 在EBM中,NLP对于管理现代医学文献的复杂性至关重要.
- 在NLP的进一步研究可以显著改进证据评估,合成和总结.
- NLP有可能彻底改变EBM工作流程,提高数据的可理解性和效率.
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