Biomedical knowledge is growing in an astounding pace with a majority of this knowledge is represented as scientific publications. Text mining tools and methods represents automatic approaches for extracting hidden patterns and trends from this semi structured and unstructured data. In Biomedical Text mining, Literature Based Discovery (LBD) is the process of automatically discovering novel associations between medical terms otherwise mentioned in disjoint literature sets. LBD approaches proven to be successfully reducing the discovery time of potential associations that are hidden in the vast amount of scientific literature. The process focuses on creating concept profiles for medical terms such as a disease or symptom and connecting it with a drug and treatment based on the statistical significance of the shared profiles. This knowledge discovery approach introduced in 1989 still remains as a core task in text mining. Currently the ABC principle based two approaches namely open discovery and closed discovery are mostly explored in LBD process. This review starts with general introduction about text mining followed by biomedical text mining and introduces various literature resources such as MEDLINE, UMLS, MESH, and SemMedDB. This is followed by brief introduction of the core ABC principle and its associated two approaches open discovery and closed discovery in LBD process. This review also discusses the deep learning applications in LBD by reviewing the role of transformer models and neural networks based LBD models and its future aspects. Finally, reviews the key biomedical discoveries generated through LBD approaches in biomedicine and conclude with the current limitations and future directions of LBD.

中文翻译：

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基于文献的发现（LBD）：生物医学文本挖掘中的假设生成和知识发现

生物医学知识正在以惊人的速度增长，其中大部分知识以科学出版物的形式呈现。文本挖掘工具和方法代表了从半结构化和非结构化数据中提取隐藏模式和趋势的自动方法。在生物医学文本挖掘中，基于文献的发现 (LBD) 是自动发现不相交文献集中提到的医学术语之间的新颖关联的过程。事实证明，LBD 方法可以成功缩短隐藏在大量科学文献中的潜在关联的发现时间。该过程的重点是为疾病或症状等医学术语创建概念概况，并根据共享概况的统计显着性将其与药物和治疗联系起来。这种 1989 年引入的知识发现方法仍然是文本挖掘的核心任务。目前LBD过程中主要探索基于ABC原则的两种方法，即开放式发现和封闭式发现。本综述首先对文本挖掘进行一般介绍，然后介绍生物医学文本挖掘，并介绍各种文献资源，例如 MEDLINE、UMLS、MESH 和 SemMedDB。接下来简要介绍了LBD过程中的核心ABC原则及其相关的开放发现和封闭发现两种方法。本综述还通过回顾 Transformer 模型和基于 LBD 模型的神经网络的作用及其未来方面，讨论了 LBD 中的深度学习应用。最后，