University of Vermont, Robert Larner, M.D. College of Medicine, 89 Beaumont Avenue, Burlington, VT, 05405, USA.
Delft University of Technology, Stevinweg 1, Delft, CB, 2628, The Netherlands.
BMC Palliat Care. 2021 Jan 25;20(1):23. doi: 10.1186/s12904-021-00716-3.
High quality serious illness communication requires good understanding of patients' values and beliefs for their treatment at end of life. Natural Language Processing (NLP) offers a reliable and scalable method for measuring and analyzing value- and belief-related features of conversations in the natural clinical setting. We use a validated NLP corpus and a series of statistical analyses to capture and explain conversation features that characterize the complex domain of moral values and beliefs. The objective of this study was to examine the frequency, distribution and clustering of morality lexicon expressed by patients during palliative care consultation using the Moral Foundations NLP Dictionary.
We used text data from 231 audio-recorded and transcribed inpatient PC consultations and data from baseline and follow-up patient questionnaires at two large academic medical centers in the United States. With these data, we identified different moral expressions in patients using text mining techniques. We used latent class analysis to explore if there were qualitatively different underlying patterns in the PC patient population. We used Poisson regressions to analyze if individual patient characteristics, EOL preferences, religion and spiritual beliefs were associated with use of moral terminology.
We found two latent classes: a class in which patients did not use many expressions of morality in their PC consultations and one in which patients did. Age, race (white), education, spiritual needs, and whether a patient was affiliated with Christianity or another religion were all associated with membership of the first class. Gender, financial security and preference for longevity-focused over comfort focused treatment near EOL did not affect class membership.
This study is among the first to use text data from a real-world situation to extract information regarding individual foundations of morality. It is the first to test empirically if individual moral expressions are associated with individual characteristics, attitudes and emotions.
高质量的重病沟通需要很好地理解患者在生命末期对治疗的价值观和信念。自然语言处理（NLP）提供了一种可靠且可扩展的方法，可用于测量和分析自然临床环境中与价值和信念相关的对话特征。我们使用经过验证的 NLP 语料库和一系列统计分析来捕捉和解释描述道德价值观和信念复杂领域的对话特征。本研究的目的是使用道德基础 NLP 词典检查在姑息治疗咨询中患者表达的道德词汇的频率、分布和聚类。
我们使用来自美国两个大型学术医疗中心的 231 份音频记录和转录的住院患者姑息治疗咨询文本数据以及基线和随访患者问卷调查数据。使用这些数据，我们使用文本挖掘技术识别患者的不同道德表达。我们使用潜在类别分析来探索姑息治疗患者人群中是否存在不同的潜在模式。我们使用泊松回归分析来分析个体患者特征、临终偏好、宗教和精神信仰是否与道德术语的使用相关。
我们发现了两个潜在类别：一类患者在其姑息治疗咨询中没有使用许多道德表达，另一类患者使用了许多道德表达。年龄、种族（白人）、教育程度、精神需求以及患者是否隶属于基督教或其他宗教，都与第一类患者的成员身份相关。性别、财务安全状况以及在生命末期更倾向于延长生命而非舒适治疗的偏好并不影响类别成员身份。
本研究首次使用来自真实情况的文本数据提取有关个人道德基础的信息。它首次通过实证检验来测试个体道德表达是否与个体特征、态度和情绪相关。
