论文标题
KG-MTT-BERT：知识图增强了多型医学文本分类的BERT
KG-MTT-BERT: Knowledge Graph Enhanced BERT for Multi-Type Medical Text Classification
论文作者
论文摘要
由于广泛采用电子健康记录（EHR）系统，医学文本学习最近成为改善医疗保健的有希望的领域。医学文本的复杂性，例如多样化的长度，混合文本类型以及充满医学术语的复杂性，对开发有效的深度学习模型构成了巨大的挑战。伯特（Bert）提出了许多NLP任务的最新结果，例如文本分类和问题答案。但是，独立的BERT模型无法处理医学文本的复杂性，尤其是冗长的临床注释。本文中，我们通过将BERT模型扩展到长长的多类文本中，并使用医学知识图的集成来开发一种称为KG-MTT-BERT（知识图增强的多类文本BERT）的新模型。我们的模型可以胜过与诊断相关组（DRG）分类中的所有基准和其他最先进的模型，这需要全面的医学文本才能准确分类。我们还证明，我们的模型可以有效地处理多类文本，并且医学知识图的集成可以显着提高性能。
Medical text learning has recently emerged as a promising area to improve healthcare due to the wide adoption of electronic health record (EHR) systems. The complexity of the medical text such as diverse length, mixed text types, and full of medical jargon, poses a great challenge for developing effective deep learning models. BERT has presented state-of-the-art results in many NLP tasks, such as text classification and question answering. However, the standalone BERT model cannot deal with the complexity of the medical text, especially the lengthy clinical notes. Herein, we develop a new model called KG-MTT-BERT (Knowledge Graph Enhanced Multi-Type Text BERT) by extending the BERT model for long and multi-type text with the integration of the medical knowledge graph. Our model can outperform all baselines and other state-of-the-art models in diagnosis-related group (DRG) classification, which requires comprehensive medical text for accurate classification. We also demonstrated that our model can effectively handle multi-type text and the integration of medical knowledge graph can significantly improve the performance.
