ABSTRACT
ObjectiveTo investigate the identification of kidney Yang deficiency syndrome of patients with osteoporosis（OP）， and to form the clinical syndrome identification rules of traditional Chinese medicine（TCM）. MethodBasic information， etiology， clinical symptoms and other characteristics of 982 OP patients were included， and statistical tests were used to screen the variables associated with kidney Yang deficiency syndrome. Taking the decision tree as the base model， bootstrap aggregation algorithm（Bagging algorithm） was utilized to establish the classification model of kidney Yang deficiency syndrome in OP， generating numerous rules and removing redundancy. Combining least absolute shrinkage and selection operator（LASSO） regression to screen key rules and integrate them to construct an identification model， achieving the identification of kidney Yang deficiency syndrome in OP patients. ResultEighteen key identification rules were screened out， and of these， where 11 rules with regression coefficients>0 correlated positively with the kidney Yang deficiency syndrome， the rule with the highest coefficient was chilliness（present）&feverish sensation over the palm and sole（absent）. The other 7 rules with regression coefficients<0 correlated negatively with the syndrome， the rule with the lowest coefficient was reddish tongue（present）&diarrhea（absent）&deficiency of endowment（absent）. According to the regression coefficients of each key rule， variables with importance>0.2 were ranked as chilliness， reddish tongue， feverish sensation over the palm and sole， cold limbs， clear urine， diarrhea， deficiency of endowment， prolonged illness. The results of the partial dependence analysis of the identification model showed that compared to OP patients without chilliness， those with chilliness（present） had a 0.266 8 higher probability of being identified as having kidney Yang deficiency syndrome， indicating that this variable had the highest impact on identification of the syndrome. Similarly， compared to OP patients without reddish tongue， those with reddish tongue had a 0.141 9 lower probability of being identified as having kidney Yang deficiency syndrome， indicating that this variable had the highest impact on identifying non-kidney Yang deficiency syndrome. The accuracy， sensitivity， specificity and area under receiver operating characteristic curve（AUC） of the established kidney Yang deficiency syndrome identification model in the test set were 0.865 9， 0.853 7， 0.872 0 and 0.931 5， respectively. ConclusionA precise identification model of OP kidney Yang deficiency syndrome is conducted basing on the rule ensemble method of Bagging combining LASSO regression， and the screened key rules can explain the identification process of kidney Yang deficiency syndrome. In this research， according to the regression coefficients of rules， the importance and partial dependence of variables， combined with the thinking of TCM， the influence of patient characteristics on the identification of syndromes is described， so as to reveal the primary and secondary syndromes of identification and assist the clinical identification of kidney Yang deficiency syndrome.
ABSTRACT
ObjectiveTo achieve high-dimensional prediction of class imbalanced of adverse drug reaction（ADR） of traditional Chinese medicine（TCM） and to classify and identify risk factors affecting the occurrence of ADR based on the post-marketing safety data of TCM monitored centrally in real world hospitals. MethodThe ensemble clustering resampling combined with regularized Group Lasso regression was used to perform high-dimensional balancing of ADR class-imbalanced data， and then to integrate the balanced datasets to achieve ADR prediction and the risk factor identification by category. ResultA practical example study of the proposed method on a monitoring data of TCM injection performed that the accuracy of the ADR prediction， the prediction sensitivity， the prediction specificity and the area under receiver operating characteristic curve（AUC） were all above 0.8 on the test set. Meanwhile， 40 risk factors affecting the occurrence of ADR were screened out from total 600 high-dimensional variables. And the effect of risk factors on the occurrence of ADR was identified by classification weighting. The important risk factors were classified as follows：past history， medication information， name of combined drugs， disease status， number of combined drugs and personal data. ConclusionIn the real world data of rare ADR with a large amount of clinical variables， this paper realized accurate ADR prediction on high-dimensional and class imbalanced condition， and classified and identified the key risk factors and their clinical significance of categories， so as to provide risk early warning for clinical rational drug use and combined drug use， as well as scientific basis for reevaluation of safety of post-marketing TCM.