Medical knowledge embedding based on recursive neural network for multi-disease diagnosis

Artificial Intelligence in Medicine
Jingchi JiangQiubin Yu

Abstract

The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.e., high-dimensional and continuous vectors) is a feasible approach to complex reasoning that can not only retain the semantic information of knowledge, but also establish the quantifiable relationship among embeddings. In this paper, we propose a recursive neural knowledge network (RNKN), which combines medical knowledge based on first-order logic with a recursive neural network for multi-disease diagnosis. After the RNKN is efficiently trained using manually annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented knowledge embeddings and weight matrixes are learned. The experimental results confirm that the diagnostic accuracy of the RNKN is superior to those of four machine learning models, four classical neural networks and Markov logic network. The results also demonstrate that the more explicit the evidence extracted from CEMRs, the ...Continue Reading

References

Sep 2, 2015·Journal of Biomedical Informatics·Simone MariniRiccardo Bellazzi
Jan 19, 2016·Computer Methods and Programs in Biomedicine·P Fuster-ParraA Aguiló
May 27, 2017·Artificial Intelligence in Medicine·Tong YuHongjie Gao
Feb 13, 2018·Computer Methods and Programs in Biomedicine·Jingchi JiangQiubin Yu
Mar 27, 2018·Artificial Intelligence in Medicine·Andres DuqueLourdes Araujo

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