Sarmet Max, Kabani Aamna, Coelho Luis, Dos Reis Sara Seabra, Zeredo Jorge L, Mehta Ambereen K
Tertiary Referral Center of Neuromuscular Diseases, Hospital de Apoio de Brasília, Brazil.
Graduate Department of Health Science and Technology, University of Brasília, Brazil.
Palliat Med. 2023 Feb;37(2):275-290. doi: 10.1177/02692163221141969. Epub 2022 Dec 10.
Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy.
To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed.
A scoping review using the framework by Arksey and O'Malley and the updated recommendations proposed by Levac et al. was conducted.
PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified.
197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings.
We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data.
在过去5年中，自然语言处理因其通用性和准确性而越来越多地用于姑息治疗研究。
评估和描述自然语言处理在姑息治疗研究中的应用，包括最常用的自然语言处理软件和计算方法、数据来源、自然语言处理应用随时间的趋势以及所涉及的姑息治疗主题。
采用Arksey和O'Malley的框架以及Levac等人提出的更新建议进行范围综述。
在PubMed、科学网、Embase、Scopus和IEEE Xplore数据库中搜索使用自然语言处理工具的姑息治疗研究。收集有关研究特征和所使用的自然语言处理工具的数据，并确定相关的姑息治疗主题。
共识别出197篇相关参考文献。其中，82篇经过全文审查后被纳入。研究发表于2007年至2022年的48种不同期刊。平均样本量为21541（中位数为435）。识别出32种不同的自然语言处理软件和33种机器学习方法。确定了纳入研究中的9个主要数据处理来源和15个主要姑息治疗主题。最常见的主题是死亡率和预后预测。我们还发现了一种趋势，即自然语言处理经常用于分析从音频记录中提取的临床重症对话。
我们发现了82篇关于姑息治疗的论文，这些论文使用自然语言处理方法研究了广泛的主题和数据来源，这可能会扩大该方法的应用。我们鼓励研究人员在未来的研究中考虑采用这种前沿的研究方法，以改进已发表的姑息治疗数据。
