Department of Medicine, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce Street, 1 Silverstein Radiology, Philadelphia, PA 19104, USA.
Radiol Clin North Am. 2021 Nov;59(6):919-931. doi: 10.1016/j.rcl.2021.06.003.
Natural language processing (NLP) is a subfield of computer science and linguistics that can be applied to extract meaningful information from radiology reports. Symbolic NLP is rule based and well suited to problems that can be explicitly defined by a set of rules. Statistical NLP is better situated to problems that cannot be well defined and requires annotated or labeled examples from which machine learning algorithms can infer the rules. Both symbolic and statistical NLP have found success in a variety of radiology use cases. More recently, deep learning approaches, including transformers, have gained traction and demonstrated good performance.
自然语言处理（NLP）是计算机科学和语言学的一个分支，可应用于从放射学报告中提取有意义的信息。符号 NLP 基于规则，非常适合可以通过一组规则明确定义的问题。统计 NLP 更适合那些无法很好定义的问题，并且需要从这些问题中进行注释或标记示例，以便机器学习算法可以推断出规则。符号 NLP 和统计 NLP 在各种放射学用例中都取得了成功。最近，深度学习方法，包括转换器，已经引起了关注，并表现出了良好的性能。
