Pham Thuan, Tao Xiaohui, Zhang Ji, Yong Jianming
University of Southern Queensland, Toowoomba, Australia.
Health Inf Sci Syst. 2020 Feb 14;8(1):10. doi: 10.1007/s13755-020-0100-6. eCollection 2020 Dec.
Applying and in building a heterogeneous information graph (HIG) to develop a classification model has achieved a notable performance in improving the accuracy of predicting the status of health risks. In this study, the approach that was used, integrated knowledge of the medical domain as well as taking advantage of applying and in building a classification model for diagnosis. The research mined knowledge which was extracted from titles and abstracts of MEDLINE to discover how to assess the links between objects relating to medical concepts. A knowledge-base HIG model then was developed for the prediction of a patient's health status. The results of the experiment showed that the knowledge-base model was superior to the baseline model and has demonstrated that the knowledge-base could help improve the performance of the classification model. The contribution of this study has been to provide a framework for applying a knowledge-base in the classification model which helps these models achieve the best performance of predictions. This study has also contributed a model to medical practice to help practitioners become more confident in making final decisions in diagnosing illness. Moreover, this study affirmed that biomedical literature could assist in building a classification model. This contribution will be advantageous for future researchers in mining the knowledge-base to develop different kinds of classification models.
在构建异构信息图（HIG）以开发分类模型时应用[具体内容缺失]，在提高健康风险状态预测准确性方面取得了显著成效。在本研究中，所采用的方法整合了医学领域知识，并利用[具体内容缺失]构建诊断分类模型。该研究挖掘了从MEDLINE的标题和摘要中提取的知识，以发现如何评估与医学概念相关的对象之间的联系。然后开发了一个知识库HIG模型来预测患者的健康状况。实验结果表明，知识库模型优于基线模型，并证明知识库有助于提高分类模型的性能。本研究的贡献在于为在分类模型中应用知识库提供了一个框架，有助于这些模型实现最佳预测性能。本研究还为医学实践贡献了一个模型，帮助从业者在疾病诊断中做出最终决策时更有信心。此外，本研究证实生物医学文献有助于构建分类模型。这一贡献将有利于未来研究人员挖掘知识库以开发不同类型的分类模型。
