Cai Linkun, Li Jia, Lv Han, Liu Wenjuan, Niu Haijun, Wang Zhenchang
School of Biological Science and Medical Engineering, Beihang University, 100191 Beijing, China.
Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050 Beijing, China.
J Biomed Inform. 2023 Jul;143:104418. doi: 10.1016/j.jbi.2023.104418. Epub 2023 Jun 7.
The past decade has witnessed an explosion of textual information in the biomedical field. Biomedical texts provide a basis for healthcare delivery, knowledge discovery, and decision-making. Over the same period, deep learning has achieved remarkable performance in biomedical natural language processing, however, its development has been limited by well-annotated datasets and interpretability. To solve this, researchers have considered combining domain knowledge (such as biomedical knowledge graph) with biomedical data, which has become a promising means of introducing more information into biomedical datasets and following evidence-based medicine. This paper comprehensively reviews more than 150 recent literature studies on incorporating domain knowledge into deep learning models to facilitate typical biomedical text analysis tasks, including information extraction, text classification, and text generation. We eventually discuss various challenges and future directions.
过去十年见证了生物医学领域文本信息的爆炸式增长。生物医学文本为医疗保健服务、知识发现和决策提供了基础。在同一时期，深度学习在生物医学自然语言处理方面取得了显著进展，然而，其发展受到标注良好的数据集和可解释性的限制。为了解决这个问题，研究人员考虑将领域知识（如生物医学知识图谱）与生物医学数据相结合，这已成为向生物医学数据集中引入更多信息并遵循循证医学的一种有前景的方法。本文全面回顾了150多篇近期关于将领域知识纳入深度学习模型以促进典型生物医学文本分析任务（包括信息提取、文本分类和文本生成）的文献研究。我们最终讨论了各种挑战和未来方向。
