Abu-Salih Bilal, Al-Qurishi Muhammad, Alweshah Mohammed, Al-Smadi Mohammad, Alfayez Reem, Saadeh Heba
The University of Jordan, Amman, Jordan.
King Saud University, Riyadh, Saudi Arabia.
J Big Data. 2023;10(1):81. doi: 10.1186/s40537-023-00774-9. Epub 2023 May 28.
The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.
在高效且有效的大数据分析解决方案需求的推动下，数据分析在医疗行业的应用取得了重大进展。知识图谱（KGs）在这一领域已被证明具有实用性，并扎根于众多医疗应用中，以提供更好的数据表示和知识推理。然而，由于缺乏具有代表性的知识图谱构建分类法，该指定领域中的一些现有方法存在不足且不够完善。本文首次提供了全面的分类法，并对医疗知识图谱构建进行了全景式审视。此外，还对从与各种医疗背景相关的学术著作中提取的当前最先进技术进行了全面研究。从用于知识提取的方法、知识库的类型和来源以及所采用的评估协议等方面对这些技术进行了严格评估。最后，报告并讨论了文献中的一些研究发现和现有问题，为这一活跃领域的未来研究开辟了视野。
