A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models-猫眼课题宝

DOI：

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发表时间：

2021-08

期刊：

AMIA ... Annual Symposium proceedings. AMIA Symposium

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其他

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作者：

Zehao Yu;Xi Yang;Chong Dang;Songzi Wu;P. Adekkanattu;Jyotishman Pathak;T. George;W. Hogan;Yi Guo;J. Bian;Yonghui Wu

社会和行为健康决定因素(SBDoH)在塑造人们的健康方面发挥着重要作用。在临床研究中，特别是比较有效性研究中，未能调整SBDoH因素可能会导致统计分析和基于机器学习的模型中的混淆问题和错误分类错误。然而，由于目前的电子健康记录(EHR)系统中缺乏结构化的SBDoH信息，因此研究SBDoH在临床结果中的因素的研究有限，而大部分SBDoH信息都记录在临床叙述中。自然语言处理(NLP)是从非结构化的临床文本中提取此类信息的关键技术。然而，目前还没有一套成熟的针对SBDoH的临床NLP系统。在这项研究中，我们研究了两个最新的基于转换器的NLP模型，包括BERT和Roberta，用于从临床叙述中提取SBDoH概念，应用性能最佳的模型提取肺癌筛查患者队列的SBDoH概念，并检查NLP提取结果和结构化EHR(从国际疾病分类代码等标准词汇表中捕获的SBDoH信息)之间的SBDoH信息的差异。实验结果表明，基于BERT的自然语言处理模型获得了最好的严格/宽松F1评分，分别为0.8791和0.8999。在864例肺癌患者的161,933份不同类型的临床记录中，NLP提取的SBDoH信息与结构化EHR的比较表明，更详细的吸烟、教育和就业信息仅在临床描述中捕捉到，需要同时使用临床描述和结构化EHR来构建患者SBDoH因素的更完整的图景。

Social and behavioral determinants of health (SBDoH) have important roles in shaping people's health. In clinical research studies, especially comparative effectiveness studies, failure to adjust for SBDoH factors will potentially cause confounding issues and misclassification errors in either statistical analyses and machine learning-based models. However, there are limited studies to examine SBDoH factors in clinical outcomes due to the lack of structured SBDoH information in current electronic health record (EHR) systems, while much of the SBDoH information is documented in clinical narratives. Natural language processing (NLP) is thus the key technology to extract such information from unstructured clinical text. However, there is not a mature clinical NLP system focusing on SBDoH. In this study, we examined two state-of-the-art transformer-based NLP models, including BERT and RoBERTa, to extract SBDoH concepts from clinical narratives, applied the best performing model to extract SBDoH concepts on a lung cancer screening patient cohort, and examined the difference of SBDoH information between NLP extracted results and structured EHRs (SBDoH information captured in standard vocabularies such as the International Classification of Diseases codes). The experimental results show that the BERT-based NLP model achieved the best strict/lenient F1-score of 0.8791 and 0.8999, respectively. The comparison between NLP extracted SBDoH information and structured EHRs in the lung cancer patient cohort of 864 patients with 161,933 various types of clinical notes showed that much more detailed information about smoking, education, and employment were only captured in clinical narratives and that it is necessary to use both clinical narratives and structured EHRs to construct a more complete picture of patients' SBDoH factors.