Denecke Kerstin, Reichenpfader Daniel
Bern University of Applied Sciences, Institute for Medical Informatics, Quellgasse 21, Biel/Bienne, 2502, Bern, Switzerland.
Bern University of Applied Sciences, Institute for Medical Informatics, Quellgasse 21, Biel/Bienne, 2502, Bern, Switzerland.
J Biomed Inform. 2023 Apr;140:104336. doi: 10.1016/j.jbi.2023.104336. Epub 2023 Mar 22.
A clinical sentiment is a judgment, thought or attitude promoted by an observation with respect to the health of an individual. Sentiment analysis has drawn attention in the healthcare domain for secondary use of data from clinical narratives, with a variety of applications including predicting the likelihood of emerging mental illnesses or clinical outcomes. The current state of research has not yet been summarized. This study presents results from a scoping review aiming at providing an overview of sentiment analysis of clinical narratives in order to summarize existing research and identify open research gaps. The scoping review was carried out in line with the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guideline. Studies were identified by searching 4 electronic databases (e.g., PubMed, IEEE Xplore) in addition to conducting backward and forward reference list checking of the included studies. We extracted information on use cases, methods and tools applied, used datasets and performance of the sentiment analysis approach. Of 1,200 citations retrieved, 29 unique studies were included in the review covering a period of 8 years. Most studies apply general domain tools (e.g. TextBlob) and sentiment lexicons (e.g. SentiWordNet) for realizing use cases such as prediction of clinical outcomes; others proposed new domain-specific sentiment analysis approaches based on machine learning. Accuracy values between 71.5-88.2% are reported. Data used for evaluation and test are often retrieved from MIMIC databases or i2b2 challenges. Latest developments related to artificial neural networks are not yet fully considered in this domain. We conclude that future research should focus on developing a gold standard sentiment lexicon, adapted to the specific characteristics of clinical narratives. Efforts have to be made to either augment existing or create new high-quality labeled data sets of clinical narratives. Last, the suitability of state-of-the-art machine learning methods for natural language processing and in particular transformer-based models should be investigated for their application for sentiment analysis of clinical narratives.
临床情感是基于对个体健康状况的观察而产生的一种判断、想法或态度。情感分析在医疗保健领域引起了关注，用于临床叙事数据的二次利用，有多种应用，包括预测新发精神疾病或临床结局的可能性。目前尚未对该研究领域的现状进行总结。本研究展示了一项范围综述的结果，旨在概述临床叙事的情感分析，以总结现有研究并识别开放的研究空白。该范围综述是按照PRISMA-ScR（系统评价和Meta分析扩展的范围综述的首选报告项目）指南进行的。除了对纳入研究进行前后向参考文献检查外，还通过搜索4个电子数据库（如PubMed、IEEE Xplore）来识别研究。我们提取了关于用例、应用的方法和工具、使用的数据集以及情感分析方法性能的信息。在检索到的1200条引文中，有29项独特的研究被纳入综述，涵盖了8年的时间。大多数研究应用通用领域工具（如TextBlob）和情感词典（如SentiWordNet）来实现诸如临床结局预测等用例；其他研究则基于机器学习提出了新的特定领域情感分析方法。报告的准确率在71.5%-88.2%之间。用于评估和测试的数据通常从MIMIC数据库或i2b2挑战中获取。该领域尚未充分考虑与人工神经网络相关的最新进展。我们得出结论，未来的研究应专注于开发适合临床叙事特定特征的黄金标准情感词典。必须努力扩充现有的或创建新的高质量临床叙事标注数据集。最后，应研究最先进的机器学习方法在自然语言处理方面的适用性，特别是基于Transformer的模型在临床叙事情感分析中的应用。
