PercolationDF: A percolation-based medical diagnosis framework

Jingchi Jiang; Xuehui Yu; Yi Lin; Yi Guan; Jingchi Jiang; Xuehui Yu; Yi Lin; Yi Guan

doi:10.3934/mbe.2022273

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 6: 5832-5849. doi: 10.3934/mbe.2022273

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Research article Special Issues

PercolationDF: A percolation-based medical diagnosis framework

1.
The Artificial Intelligence Institute, Harbin Institute of Technology, Harbin, China
2.
The Faculty of Computing, Harbin Institute of Technology, Harbin, China

Academic Editor: Simon James Fong

Received: 18 December 2021 Revised: 16 March 2022 Accepted: 22 March 2022 Published: 06 April 2022

Goal: With the continuing shortage and unequal distribution of medical resources, our objective is to develop a general diagnosis framework that utilizes a smaller amount of electronic medical records (EMRs) to alleviate the problem that the data volume requirement of prevailing models is too vast for medical institutions to afford. Methods: The framework proposed contains network construction, network expansion, and disease diagnosis methods. In the first two stages above, the knowledge extracted from EMRs is utilized to build and expense an EMR-based medical knowledge network (EMKN) to model and represent the medical knowledge. Then, percolation theory is modified to diagnose EMKN. Result: Facing the lack of data, our framework outperforms naïve Bayes networks, neural networks and logistic regression, especially in the top-10 recall. Out of 207 test cases, 51.7% achieved 100% in the top-10 recall, 21% better than what was achieved in one of our previous studies. Conclusion: The experimental results show that the proposed framework may be useful for medical knowledge representation and diagnosis. The framework effectively alleviates the lack of data volume by inferring the knowledge modeled in EMKN. Significance: The proposed framework not only has applications for diagnosis but also may be extended to other domains to represent and model the knowledge and inference on the representation.
- complex networks,
- knowledge representation,
- medical diagnosis,
- percolation theory
Citation: Jingchi Jiang, Xuehui Yu, Yi Lin, Yi Guan. PercolationDF: A percolation-based medical diagnosis framework[J]. Mathematical Biosciences and Engineering, 2022, 19(6): 5832-5849. doi: 10.3934/mbe.2022273

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Abstract

Goal: With the continuing shortage and unequal distribution of medical resources, our objective is to develop a general diagnosis framework that utilizes a smaller amount of electronic medical records (EMRs) to alleviate the problem that the data volume requirement of prevailing models is too vast for medical institutions to afford. Methods: The framework proposed contains network construction, network expansion, and disease diagnosis methods. In the first two stages above, the knowledge extracted from EMRs is utilized to build and expense an EMR-based medical knowledge network (EMKN) to model and represent the medical knowledge. Then, percolation theory is modified to diagnose EMKN. Result: Facing the lack of data, our framework outperforms naïve Bayes networks, neural networks and logistic regression, especially in the top-10 recall. Out of 207 test cases, 51.7% achieved 100% in the top-10 recall, 21% better than what was achieved in one of our previous studies. Conclusion: The experimental results show that the proposed framework may be useful for medical knowledge representation and diagnosis. The framework effectively alleviates the lack of data volume by inferring the knowledge modeled in EMKN. Significance: The proposed framework not only has applications for diagnosis but also may be extended to other domains to represent and model the knowledge and inference on the representation.

References

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