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Open AccessArticle

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

by Gang Du 1, Yinan Shi 1, Aijun Liu 2,* and Taoning Liu 2
1
School of Business and Administration, Faculty of Economics and Management, East China Normal University, Shanghai 200062, China
2
Department of Management Engineering, School of Economics & Management, Xidian University, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1191; https://doi.org/10.3390/e21121191
Received: 30 November 2019 / Revised: 30 November 2019 / Accepted: 1 December 2019 / Published: 4 December 2019
With the continuous development of data mining techniques in the medical field, variance analysis in clinical pathways based on data mining approaches have attracted increasing attention from scholars and decision makers. However, studies on variance analysis and treatment of specific kinds of disease are still relatively scarce. In order to reduce the hazard of postpartum hemorrhage after cesarean section, we conducted a detailed analysis on the relevant risk factors and treatment mechanisms, adopting the integrated Bayesian network and association rule mining approaches. By proposing a Bayesian network model based on regression analysis, we calculated the probability of risk factors determining the key factors that result in postpartum hemorrhage after cesarean section. In addition, we mined a few association rules regarding the treatment of postpartum hemorrhage on the basis of different clinical features. We divided the risk factors into primary and secondary risk factors by realizing the classification of different causes of postpartum hemorrhage after cesarean section and sorted the posterior probability to obtain the key factors in the primary and secondary risk factors: uterine atony and prolonged labor. The rules of clinical features associated with the management of postpartum hemorrhage during cesarean section were obtained. Finally, related strategies were proposed for improving medical service quality and enhancing the rescue efficiency of clinical pathways in China.
Keywords: clinical pathways; data mining; risk factors; variance treatment clinical pathways; data mining; risk factors; variance treatment
MDPI and ACS Style

Du, G.; Shi, Y.; Liu, A.; Liu, T. Variance Risk Identification and Treatment of Clinical Pathway by Integrated Bayesian Network and Association Rules Mining. Entropy 2019, 21, 1191.

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