Clinical Relation Extraction Using Transformer-based Models (2107.08957v2)

Abstract: The newly emerged transformer technology has a tremendous impact on NLP research. In the general English domain, transformer-based models have achieved state-of-the-art performances on various NLP benchmarks. In the clinical domain, researchers also have investigated transformer models for clinical applications. The goal of this study is to systematically explore three widely used transformer-based models (i.e., BERT, RoBERTa, and XLNet) for clinical relation extraction and develop an open-source package with clinical pre-trained transformer-based models to facilitate information extraction in the clinical domain. We developed a series of clinical RE models based on three transformer architectures, namely BERT, RoBERTa, and XLNet. We evaluated these models using 2 publicly available datasets from 2018 MADE1.0 and 2018 n2c2 challenges. We compared two classification strategies (binary vs. multi-class classification) and investigated two approaches to generate candidate relations in different experimental settings. In this study, we compared three transformer-based (BERT, RoBERTa, and XLNet) models for relation extraction. We demonstrated that the RoBERTa-clinical RE model achieved the best performance on the 2018 MADE1.0 dataset with an F1-score of 0.8958. On the 2018 n2c2 dataset, the XLNet-clinical model achieved the best F1-score of 0.9610. Our results indicated that the binary classification strategy consistently outperformed the multi-class classification strategy for clinical relation extraction. Our methods and models are publicly available at https://github.com/uf-hobi-informatics-lab/ClinicalTransformerRelationExtraction. We believe this work will improve current practice on clinical relation extraction and other related NLP tasks in the biomedical domain.

Exploring Transformer-Based Models for Clinical Relation Extraction

The paper "Clinical Relation Extraction Using Transformer-based Models" presents a meticulous analysis of utilizing transformer architectures for the task of relation extraction (RE) within clinical narratives. Relation extraction is pivotal for deciphering semantic links among clinical concepts, thereby playing a crucial role in constructing comprehensive patient profiles from the unstructured data in Electronic Health Records (EHRs).

Background and Significance

In the context of NLP within the biomedical domain, RE is increasingly crucial for applications such as clinical decision support and knowledge base construction. Historically, methods for RE have transitioned from rule-based systems and traditional machine learning approaches to deep learning models. However, despite advances in concept extraction, the challenge of efficiently extracting relations continues to demand research attention. This paper's focus on transformer architectures, particularly BERT, RoBERTa, and XLNet, represents a significant step in addressing this gap in biomedical NLP.

Methodological Approach

This work systematically evaluates these three transformer architectures by assessing their performance on two clinical RE datasets—the 2018 MADE1.0 and the 2018 n2c2 challenge datasets. These datasets comprise richly annotated clinical narratives, providing robust test beds for RE models. The paper contrasts binary and multi-class classification strategies and explores techniques for handling cross-sentence relations. It also examines how best to integrate the contextual representations generated by transformers for relation classification tasks.

Key Findings

The paper's findings are quantitatively robust, underscoring the efficacy of transformer-based models pre-trained on clinical text for enhancing RE tasks. Specifically:

• RoBERTa-clinical and XLNet-clinical emerged as top performers, achieving F1-scores of 0.8959 for the MADE1.0 dataset and 0.9610 for the n2c2 dataset, respectively. This reflects an improvement over previous benchmarks, attesting to the potential gains from leveraging domain-specific pretraining.
• Binary classification was found to generally outperform multi-class classification, with observed performance gains of approximately 0.3% and 1.3% on the MADE1.0 and n2c2 datasets, respectively. This may relate to enhanced positive sample representation in binary setups.
• Cross-sentence relation handling remains a complex challenge. While the performance of the UNIFIED and DISTANCE-SPECIFIC approaches did not significantly differ in overall F1-scores, handling of cross-sentence relations with higher distances brought noise rather than benefit, indicating a need for refined strategies.

Implications and Future Directions

This research underscores the transformative potential of specialized transformer models in the medical domain, particularly in relation to RE tasks. By showcasing the superior performance of models pre-trained on clinical corpora, the paper advocates for a tailored approach to model pretraining in biomedical NLP. Furthermore, the insights into classification strategies and relation representation schemes offer concrete guidance for future modifications and optimizations in clinical NLP pipelines.

Future research directions could venture into addressing limitations like the skewed negative-positive sample ratio in cross-sentence relation scenarios. Additionally, exploring further enhancements of transformer model architectures and integrating auxiliary biomedical knowledge bases could facilitate more nuanced RE performance.

The open-source release of the pretrained models and RE package reflects a commitment to community resource sharing, thereby enabling broader application and further innovation in clinical NLP and RE endeavors.