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.

本文提出了一种基于提示的机器阅读理解（MRC）架构，能够解决临床概念抽取和关系抽取问题，在跨机构应用中表现良好，并使用转换器模型对其进行探索。在 2018 年国家 NLP 临床挑战和 2022 年 NLP 临床挑战的基准数据集上，该方法均取得了优秀的性能，并适用于处理嵌套 / 重叠概念和跨机构应用。

基于提示式机器阅读理解的临床概念和关系抽取

Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension

Automatic extraction of clinical concepts is an essential step for turning
the unstructured data within a clinical note into structured and actionable
information. In this work, we propose a clinical concept extraction model for
automatic annotation of clinical problems, treatments, and tests in clinical
notes utilizing domain-specific contextual word embedding. A contextual word
embedding model is first trained on a corpus with a mixture of clinical reports
and relevant Wikipedia pages in the clinical domain. Next, a bidirectional
LSTM-CRF model is trained for clinical concept extraction using the contextual
word embedding model. We tested our proposed model on the I2B2 2010 challenge
dataset. Our proposed model achieved the best performance among reported
baseline models and outperformed the state-of-the-art models by 3.4% in terms
of F1-score.

本研究提出一个基于领域特定词汇上下文嵌入的临床问题、治疗和测试自动标注临床笔记的模型，使用上下文嵌入模型训练一个双向 LSTM-CRF 模型用于临床概念提取，并在 I2B2 2010 挑战数据集上进行了测试，最终性能优于现有模型 3.4%。

基于上下文词嵌入的临床概念抽取

Clinical Concept Extraction with Contextual Word Embedding

This study investigates the use of unsupervised word embeddings and sequence
features for sample representation in an active learning framework built to
extract clinical concepts from clinical free text. The objective is to further
reduce the manual annotation effort while achieving higher effectiveness
compared to a set of baseline features. Unsupervised features are derived from
skip-gram word embeddings and a sequence representation approach. The
comparative performance of unsupervised features and baseline hand-crafted
features in an active learning framework are investigated using a wide range of
selection criteria including least confidence, information diversity,
information density and diversity, and domain knowledge informativeness. Two
clinical datasets are used for evaluation: the i2b2/VA 2010 NLP challenge and
the ShARe/CLEF 2013 eHealth Evaluation Lab. Our results demonstrate significant
improvements in terms of effectiveness as well as annotation effort savings
across both datasets. Using unsupervised features along with baseline features
for sample representation lead to further savings of up to 9% and 10% of the
token and concept annotation rates, respectively.

该研究采用无监督词嵌入和序列特征来提取临床自由文本中的临床概念，通过主动学习框架来进一步减少手动注释工作量，实现了较高的有效性和标注效率节约。