Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538
Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538 Zhonghua+Wei+Zhong+Bing+Ji+Jiu+Yi+Xue 2020 ; 32 (9): 1045-1050 Nephropedia Template TP
gab.com Text
Twit Text FOAVip
Twit Text #
English Wikipedia
Correlation between symptoms and their contribution to syndrome based on association rule combined with Bayesian network: syndrome of lung damp-heat accumulation in coronavirus disease 2019 #MMPMID33081888
Li J; Chun L; Feng Z; Zhao H; Xie Y; Sun B; Liu W
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue 2020[Sep]; 32 (9): 1045-1050 PMID33081888show ga
OBJECTIVE: To explore the correlation between symptoms and their contribution to syndrome based on syndrome of lung damp-heat accumulation in coronavirus disease 2019 (COVID-19), thus to provide methodological basis for the syndrome diagnosis. METHODS: Based on 654 clinical investigation questionnaires data of COVID-19 patients, a model based on syndrome of lung damp-heat accumulation was set. Using SPSS Modeler 14.1 software, association rules and Bayesian network were applied to explore the correlation between symptoms and their contribution to syndrome. RESULTS: There were 121 questionnaires referring to syndrome of lung damp-heat accumulation in total 654 questionnaires. The symptoms with frequency > 40% were fever (53.72%), cough (47.93%), red tongue (45.45%), rapid pulse (43.80%), greasy fur (42.15%), yellow tongue (41.32%), fatigue (40.50%) and anorexia (40.50%). Association rule analysis showed that the symptom groups with strong binomial correlation included fever, thirst, chest tightness, shortness of breath, cough, yellow phlegm, etc. The symptom groups with strong trinomial correlation included cough, yellow phlegm, phlegm sticky, anorexia, vomiting, heavy head and body, fever, thirst, fatigue, etc. Based on SPSS Modeler 14.1 software, with syndrome of lung damp-heat accumulation (yes = 1, no = 0) as target variable, and the selected symptoms with frequency > 15.0% as input variables, the Bayesian network model was established to obtain the probability distribution table of symptoms (groups), in which there was only one parent node (the upper node of each input variable) of fever, and the conditional probability was 0.54. The parent node of cough had yellow phlegm and syndrome of lung damp-heat accumulation, indicating that there was a direct causal relationship between cough and yellow phlegm in syndrome of lung damp-heat accumulation, and the conditional probability of cough was 0.99 under the condition of yellow phlegm. The common symptom groups and their contribution to syndrome were as follows: fever and thirsty (0.47), cough and yellow phlegm (0.49), chest tightness and polypnea (0.46), anorexia and heavy cumbersome head and body (0.61), yellow greasy fur and slippery rapid pulse (0.95). CONCLUSIONS: It is feasible and objective to analyze the correlation between symptoms and their contribution to syndromes by association rules combined with Bayesian network. It could provide methodological basis for the syndrome diagnosis.
Zhonghua+Wei+Zhong+Bing+Ji+Jiu+Yi+Xue 2020 ; 32 (9): 1045-1050
