Patient Safety Translational Research Centre, Imperial College of Science Technology and Medicine, London, UK
Patient Safety Translational Research Centre, Imperial College of Science Technology and Medicine, London, UK.
BMJ Health Care Inform. 2021 Mar;28(1). doi: 10.1136/bmjhci-2020-100262.
Unstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.
Databases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.
Nineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.
NLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.
非结构化的自由文本患者反馈包含丰富的信息，而手动分析这些数据需要大量的人力资源，这在大多数医疗保健组织中是无法提供的。本研究旨在对使用自然语言处理（NLP）和机器学习（ML）来处理和分析自由文本患者体验数据的文献进行系统回顾。
系统地搜索数据库，以确定 2000 年 1 月至 2019 年 12 月期间发表的关于使用 NLP 分析自由文本患者反馈的文章。由于研究的性质不同，因此认为叙述性综合最适合。记录与研究目的、语料库、方法、性能指标和质量指标相关的数据。
共纳入 19 篇文章。大多数（80%）研究应用语言分析技术对社交媒体网站（非请求）上的患者反馈进行分析，其次是对结构化调查（请求）进行分析。经常使用监督学习（n=9），其次是无监督（n=6）和半监督（n=3）。从社交媒体中提取的评论使用无监督方法进行分析，而在结构化调查中持有的自由文本评论使用监督方法进行分析。报告的性能指标包括精度、召回率和 F 度量，支持向量机和朴素贝叶斯是表现最好的机器学习分类器。
NLP 和 ML 已成为处理非结构化自由文本的重要工具。监督和无监督方法都有其作用，具体取决于数据源。随着数据分析工具的进步，这些技术可能对医疗保健组织有用，可以从大量非结构化的自由文本数据中生成见解。
