Variance Risk Identification and Treatment of Clinical Pathway by Integrated Bayesian Network and Association Rules Mining

Du, Gang; Shi, Yinan; Liu, Aijun; Liu, Taoning

doi:10.3390/e21121191

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…An analysis of the crucial risk factors and treatment mechanisms, following the integrated Bayesian network, then applying association rule algorithms has been confronted by Du et al [23].…”

Section: Related Workmentioning

confidence: 99%

Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

Al-Akhras¹,

Darwish²,

Atawneh³

et al. 2022

Computers, Materials &Amp; Continua

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Association rules' learning is a machine learning method used in finding underlying associations in large datasets. Whether intentionally or unintentionally present, noise in training instances causes overfitting while building the classifier and negatively impacts classification accuracy. This paper uses instance reduction techniques for the datasets before mining the association rules and building the classifier. Instance reduction techniques were originally developed to reduce memory requirements in instance-based learning. This paper utilizes them to remove noise from the dataset before training the association rules classifier. Extensive experiments were conducted to assess the accuracy of association rules with different instance reduction techniques, namely: Decremental Reduction Optimization Procedure (DROP) 3, DROP5, ALL K-Nearest Neighbors (ALLKNN), Edited Nearest Neighbor (ENN), and Repeated Edited Nearest Neighbor (RENN) in different noise ratios. Experiments show that instance reduction techniques substantially improved the average classification accuracy on three different noise levels: 0%, 5%, and 10%. The RENN algorithm achieved the highest levels of accuracy with a significant improvement on seven out of eight used datasets from the University of California Irvine (UCI) machine learning repository. The improvements were more apparent in the 5% and the 10% noise cases. When RENN was applied, the average classification accuracy for the eight datasets in the zero-noise test enhanced from 70.47% to 76.65% compared to the original test. The average accuracy was improved from 66.08% to 77.47% for the 5%-noise case and from 59.89% to 77.59% in the 10%-noise case. Higher confidence was also reported in building the association rules when RENN was used. The above results indicate that RENN is a good solution in removing noise and avoiding overfitting during the construction of the association rules classifier, especially in noisy domains.

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Section: Related Workmentioning

confidence: 99%

Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

Al-Akhras¹,

Darwish²,

Atawneh³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…Several studies analyzed and managed clinical pathway variance based on semantic web technologies, process mining, data mining and ECA modeling. However, current research on predicting clinical pathway variance is still insufficient, and is limited to a few diseases and with low prediction accuracy [78].…”

Section: Conclusion and Prospectsmentioning

confidence: 99%

Variance Analysis and Handling of Clinical Pathway: An Overview of the State of Knowledge

Huang

Zhou

2020

IEEE Access

Self Cite

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Clinical pathway is a multi-disciplinary treatment plan and work mode, which is favorable for improving healthcare service quality and reducing medical costs. Most of references demonstrate that variance analysis and handling is the key to clinical pathway management. Thus, the clinical pathway variance has become the focus of scholars. This paper uses the text mining technique to present a literature review of 496 academic articles in the field of clinical pathway variance analysis and handling, which published between 1994 and 2018. Moreover, this paper conducts a bibliometric analysis to visualize the clinical pathway variance research. In variance analysis and handling, there are a lot of imprecise knowledge and fuzzy relations to be reasoned with knowledge of different domains. In this study, methods of clinical pathway variance analysis and handling are illustrated. In addition, this paper points out the limitations of each method. Based on the results, the future prospects of clinical pathway variance analysis and handling research is proposed.INDEX TERMS Clinical pathway, processing methods, text mining, variance. III. TEMPORAL, SPATIAL AND AUTHOR DISTRIBUTION OF CLINICAL VARIANCE RESEARCH A. TEMPORAL DISTRIBUTION

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Term dependency extraction using rule-based Bayesian Network for medical image retrieval

Ayadi

Torjmen-Khemakhem

Huang

2023

Artificial Intelligence in Medicine

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Variance Risk Identification and Treatment of Clinical Pathway by Integrated Bayesian Network and Association Rules Mining

Cited by 4 publications

References 33 publications

Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

Variance Analysis and Handling of Clinical Pathway: An Overview of the State of Knowledge

Term dependency extraction using rule-based Bayesian Network for medical image retrieval

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