Objective: To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehension (MRC) architecture with good generalizability for cross-institution applications. Methods: We formulate both clinical concept extraction and relation extraction using a unified prompt-based MRC architecture and explore state-of-the-art transformer models. We compare our MRC models with existing deep learning models for concept extraction and end-to-end relation extraction using two benchmark datasets developed by the 2018 National NLP Clinical Challenges (n2c2) challenge (medications and adverse drug events) and the 2022 n2c2 challenge (relations of social determinants of health [SDoH]). We also evaluate the transfer learning ability of the proposed MRC models in a cross-institution setting. We perform error analyses and examine how different prompting strategies affect the performance of MRC models. Results and Conclusion: The proposed MRC models achieve state-of-the-art performance for clinical concept and relation extraction on the two benchmark datasets, outperforming previous non-MRC transformer models. GatorTron-MRC achieves the best strict and lenient F1-scores for concept extraction, outperforming previous deep learning models on the two datasets by 1%~3% and 0.7%~1.3%, respectively. For end-to-end relation extraction, GatorTron-MRC and BERT-MIMIC-MRC achieve the best F1-scores, outperforming previous deep learning models by 0.9%~2.4% and 10%-11%, respectively. For cross-institution evaluation, GatorTron-MRC outperforms traditional GatorTron by 6.4% and 16% for the two datasets, respectively. The proposed method is better at handling nested/overlapped concepts, extracting relations, and has good portability for cross-institute applications.

翻译：目的：开发一种自然语言处理系统，在统一的基于提示的机器阅读理解（MRC）架构中同时解决临床概念抽取和关系抽取问题，并具备跨机构应用的良好泛化能力。方法：我们采用统一的基于提示的MRC架构来形式化临床概念抽取和关系抽取，并探索最先进的Transformer模型。我们使用2018年国立NLP临床挑战赛（n2c2）（药物和不良药物事件）和2022年n2c2挑战赛（健康社会决定因素[SDoH]关系）开发的两个基准数据集，将我们的MRC模型与现有的深度学习模型进行概念抽取和端到端关系抽取的比较。我们还评估了所提出的MRC模型在跨机构环境下的迁移学习能力，进行了误差分析，并考察了不同提示策略对MRC模型性能的影响。结果与结论：所提出的MRC模型在两个基准数据集上均实现了临床概念和关系抽取的最先进性能，超越了以往非MRC的Transformer模型。GatorTron-MRC在概念抽取中取得了最佳严格和宽松F1分数，在两个数据集上分别比以往的深度学习模型提高了1%~3%和0.7%~1.3%。对于端到端关系抽取，GatorTron-MRC和BERT-MIMIC-MRC取得了最佳F1分数，分别比以往的深度学习模型提高了0.9%~2.4%和10%~11%。在跨机构评估中，GatorTron-MRC在两个数据集上分别比传统GatorTron提高了6.4%和16%。所提出的方法更善于处理嵌套/重叠概念、抽取关系，并具有良好的跨机构应用可移植性。