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causality assessment ( ca ) , is a method of evaluation used in pharmacovigilance to find out the relationship between drugs exposed and reported adverse drug reactions ( adr ) .
it includes , finding the temporal relationship between drugs and reported adr , dechallenge , rechallenge , clinical and pathological characteristics of the events .
it is difficult for the practitioner with careful monitoring to identify the drugs causing adr .
in such a condition the withdrawal of drugs one at a time and evaluating the reaction of dechallenge has become essential .
dechallenge is a response observed in a patient such as reduction or disappearance of adr after withdrawal of a drug .
there are two types of dechallenge namely positive dechallenge which resolves with the withdrawal of drug and the negative dechallenge which follows a course of its own .
decision on the withdrawal of drug has been considered from the point of adr underlying the disease .
a typical procedure has to be followed before attempting rechallenge , with an understanding of risk involved for the patient .
so the prescribers and the patients may not come forward for the procedures involved except on a few occasions .
the prescribers too prefer to adopt dechallenge rather than rechallenge . detecting adverse drug reactions and dechallenge
data mining is a kind of statistical approach for discovering useful patterns from enormous amount of data .
it contains algorithms to find out the pattern by means of various approaches like classification , prediction , clustering and association .
data - mining algorithm called multi - item gamma position shrinker is now used for signaling potential adrs by food and drug administration ( fda ) in us .
the naive bayes ( nb ) a statistical classifier predicts class membership probabilities widely used by researchers in data mining for classification . it is assumed that all the variables contributing toward classification are mutually independent .
medical dictionary for regulatory activities ( meddra ) , an international medical terminology developed under the auspices of the international conference of harmonization of technical requirements for registration of pharmaceuticals for human use ( ich ) is a controlled medical vocabulary for describing adverse events with five levels : the coarsest is system organ class ( soc ) , followed by high level group term ( hlgt ) , higher level term ( hlt ) , preferred term ( pt ) , and lowest level term ( llt ) , the finest grained description .
the duplicate reports were deleted in accordance to fda 's recommendation of adopting in recent case number as described in one of the files  asc - nts.doc from the website of the fda .
fda had stored diseases category at preferred terms ( pt ) level . among the five levels of adverse events hierarchy of meddra
, soc level was used to classify the diseases category by referring to cancer therapy evaluation program simplified disease classification v4.0 ( meddra v 12.0 ) .
researchers suggested that it might be more advantageous to perform data mining , using a coarser grained adverse event representation soc than pt level .
the data were loaded from fda 's text file to oracle database using extract , transform and load ( etl ) tools .
records with soc as gastrointestinal , renal and urinary , metabolism and nutrition disorders were considered for dechallenge classification .
the attributes considered to evaluate dechallenge were the diseases categories denoted as system organ class ( soc ) in meddra , drug with valid trade and verbatim name represented by code 1 and 2 by fda , outcoming like life - threatening ( lt ) , death ( de ) , congenital anomaly ( ca ) , hospitalization - initial or prolonged ( ho ) , disability ( ds ) , required intervention ( ri ) to prevent permanent impairment had been considered for determining the occurrence of dechallenge .
the process of determining suitable algorithm for detecting dechallenge was accomplished by comparing nb and nb .
the performance of data mining algorithm was estimated by the parameters like percentage of accuracy , error , precision and receiver operating characteristic ( roc ) curve .
all the parameters were depicted by 2  2 confusion matrixes , containing the total number of true positive ( tp ) , true negative ( tn ) , false positive ( fp ) and false negative ( fn ) , where positive referred to identified set and negative to rejected set .
accuracy , precision and error were calculated by formula 1 , 2 and 3 as follows : roc curve was used to convey the graphical representation of perfect , liberal , random and conservative performance of an algorithm .
statistical data mining techniques had been implemented in the field of post - marketing surveillance .
safety signal detection problems in pharmacovigilance were examined by roy and jeffrey et al . , statistical data sources and data mining methods used in safety signal were studied by atsuko and manfred et al . , liang and rongzhan et al .
corani and zaffalon proposed an extension of nb named as naive credal classifier to issue reliable classifications for a domain with high uncertain information .
zhang et al . , proposed a novel model called hidden naive bayes to avoid computational complexity . for data mining model , each patient
the disease category was denoted in soc level , by mapping the pt of fda with meddra pt using extensible markup language ( xml ) mapping .
nb theorem given in formula 4 had been used to calculate the probability of an outcome .
the class label attribute dechallenge had two distinct values ( yes , no ) represented by hypothesis ( h ) .
p(h / x ) is the posterior probability where hypothesis ( h ) represents the presence of dechallenge with x as known disease category , drug code and outcome .
p ( x / h ) is the posterior probability of x on the subject of h. p ( h ) is the prior probability of h regardless of disease category , drug code and outcome .
p(x ) is the prior probability of x. for calculating the prior probability p ( x ) , dechallenge record sets with 
then the posterior probability was calculated based on outcome , disease category and drug code .
the data set of 2011 and 2012 records contained the constraints mentioned for the failure of naive bayes classifier . to overcome this , nb algorithm proposed by balamurugan et al . , was applied in the present study as detailed below .
this algorithm starts with the influence factor as the first step to determine the dependability of an attribute value on the class attribute .
influence factor was calculated for the attributes drug code , disease category and outcome on the class label dechallenge .
where i(x / ci)=influence factor n(x\ci ) = number of records in which attribute value x had the class label ci and n(ci)=total number of records in which the class label were ci .
statistical data mining techniques had been implemented in the field of post - marketing surveillance .
safety signal detection problems in pharmacovigilance were examined by roy and jeffrey et al . , statistical data sources and data mining methods used in safety signal were studied by atsuko and manfred et al . , liang and rongzhan et al .
corani and zaffalon proposed an extension of nb named as naive credal classifier to issue reliable classifications for a domain with high uncertain information .
zhang et al . , proposed a novel model called hidden naive bayes to avoid computational complexity . for data mining model , each patient
the disease category was denoted in soc level , by mapping the pt of fda with meddra pt using extensible markup language ( xml ) mapping . the fundamental assumption to attribute independence
nb theorem given in formula 4 had been used to calculate the probability of an outcome .
the class label attribute dechallenge had two distinct values ( yes , no ) represented by hypothesis ( h ) .
p(h / x ) is the posterior probability where hypothesis ( h ) represents the presence of dechallenge with x as known disease category , drug code and outcome .
p ( x / h ) is the posterior probability of x on the subject of h. p ( h ) is the prior probability of h regardless of disease category , drug code and outcome .
p(x ) is the prior probability of x. for calculating the prior probability p ( x ) , dechallenge record sets with 
then the posterior probability was calculated based on outcome , disease category and drug code .
the data set of 2011 and 2012 records contained the constraints mentioned for the failure of naive bayes classifier . to overcome this , nb algorithm proposed by balamurugan et al . , was applied in the present study as detailed below .
this algorithm starts with the influence factor as the first step to determine the dependability of an attribute value on the class attribute .
influence factor was calculated for the attributes drug code , disease category and outcome on the class label dechallenge .
/ ci)=influence factor n(x\ci ) = number of records in which attribute value x had the class label ci and n(ci)=total number of records in which the class label were ci .
statistical data mining techniques had been implemented in the field of post - marketing surveillance .
safety signal detection problems in pharmacovigilance were examined by roy and jeffrey et al . , statistical data sources and data mining methods used in safety signal were studied by atsuko and manfred et al . , liang and rongzhan et al .
corani and zaffalon proposed an extension of nb named as naive credal classifier to issue reliable classifications for a domain with high uncertain information .
zhang et al . , proposed a novel model called hidden naive bayes to avoid computational complexity .
the other data were outcome of drug and disease category . drug classified by fda as 1 for valid trade and 2 for verbatim name .
the disease category was denoted in soc level , by mapping the pt of fda with meddra pt using extensible markup language ( xml ) mapping .
nb theorem given in formula 4 had been used to calculate the probability of an outcome .
the class label attribute dechallenge had two distinct values ( yes , no ) represented by hypothesis ( h ) .
p(h / x ) is the posterior probability where hypothesis ( h ) represents the presence of dechallenge with x as known disease category , drug code and outcome .
p ( x / h ) is the posterior probability of x on the subject of h. p ( h ) is the prior probability of h regardless of disease category , drug code and outcome .
p(x ) is the prior probability of x. for calculating the prior probability p ( x ) , dechallenge record sets with 
then the posterior probability was calculated based on outcome , disease category and drug code .
the data set of 2011 and 2012 records contained the constraints mentioned for the failure of naive bayes classifier . to overcome this , nb algorithm proposed by balamurugan et al . , was applied in the present study as detailed below .
this algorithm starts with the influence factor as the first step to determine the dependability of an attribute value on the class attribute .
influence factor was calculated for the attributes drug code , disease category and outcome on the class label dechallenge .
where i(x / ci)=influence factor n(x\ci ) = number of records in which attribute value x had the class label ci and n(ci)=total number of records in which the class label were ci .
it is observed from table 1 , influence factor is high for outcomes such as ho and lt , drug code with code 1 and disease category such as gastrointestinal disorder .
yes for combinations like gastrointestinal disorder with outcome as ho and lt and for drugs with code 1 . hence classifying the dechallenge for unknown records with same combinations of attributes can be predicted as 
influence factor analysis of outcome , disease category , and drug code with dechallenge performance analysis of nb and nb provided in table 2 , where the average accuracy of nb is 90.11 and nb is 70.25 , average error of nb is 19.8 percent higher than nb , and precision of nb is 7.4 percent higher than nb . it is observed from experimental result , nb performs well in case of attributes with categorical values and zero probability issue . in the roc graph shown in figure 1 ,
although nb fits in the category of liberal performance with true positive rate as ( .7 , .8 , .9 , .9 ) , there is also substantial number of false positive rate ( .6 , .7 , .6 , .5 ) ; whereas nb fits in perfect performance with true positive rate as ( .9 , .9 , .9 , .9 ) , and minimal false positive rate as ( .2 , .8 , .0 , 0 ) .
comparison of performance analysis of error , precision and accuracy of nb and nb receiver operating characteristic curve for improved nave bayes and naive bayes
the fda uses data mining to screen the aers database using bayesian protocol for the presence of disproportionality in large adverse event - drug product pairs , but the data must be evaluated to determine causality reviews like dechallenge .
the performance of any data - mining algorithm depends on the type of attributes and its application .
a common means of identifying the association between drug and disease in pharmacovigilance is through disproportionality analysis .
this produces the results based on 2  2 tables as there are relevant drug and adr combination .
hence for large amount of data , this method will produce more number of tables which reduces the effectiveness of the approach .
several studies reported the need of data mining algorithms to review the data to make authoritative conclusion .
many studies reported the usage of mining algorithms like proportional reporting ratio , multi - item gamma - poisson shrinker .
further investigation of statistical methods to analyze large amount of data is essential to improve the effectiveness of pharmacovigilance activities .
hence in this study the data mining algorithms like nb and nb for determining the performance of algorithms in enormous data for detecting dechallenge have been investigated . among the 26 soc disease categories ,
the results presented here are based on 3 socs such as gastrointestinal , metabolism and nutrition , renal and urinary disorders . when nb is used to detect the dechallenge ,
the posterior probability is zero for records with outcome as  ca and disease category as gastrointestinal disorder .
hence 72 records of the year 2011 fourth quarter are classified as  unknown by nb algorithm .
the algorithm fails when the probability of a particular outcome or disease is uniformly distributed .
nb resolved zero probability issue by determining attributes with high influence factor and thus reducing the noise present in data for effective detection of dechallenge .
nb can be applied to any dataset which suffers from zero probability exertion . from the experimental analysis ,
it is clear that nb can be used for large data set in detecting causality reviews . for the fda
record sets , nb produced higher accuracy than nb and detected the dechallenge value as  yes  for drugs with code 1 and gastrointestinal disorder with outcome as ho and lt .
influence factor analysis in nb proved the usage of this algorithm in pharmacovigilance for predicting unknown samples .
most data available in fda and world health organisation ( who ) have neither brought in health science education , nor trained to utilise for patient care purpose .
post - marketing surveillance techniques like detecting dechallenge will help the practitioners and prescribers to gain knowledge about drugs with various reactions .
the outcome of the classification algorithms show that nb outperformed nb in traditional interesting measures like accuracy and minimal error in classifying dechallenge .
aim : dechallenge is a response observed for the reduction or disappearance of adverse drug reactions ( adr ) on withdrawal of a drug from a patient . currently available algorithms to detect dechallenge have limitations . 
 hence , there is a need to compare available new methods . to detect dechallenge in spontaneous reporting systems , data - mining algorithms like naive bayes and improved 
 naive bayes were applied for comparing the performance of the algorithms in terms of accuracy and error . 
 analyzing the factors of dechallenge like outcome and disease category will help medical practitioners and pharmaceutical industries to determine the reasons for dechallenge in order to take essential steps toward drug safety.materials and methods : adverse drug reactions of the year 2011 and 2012 were downloaded from the united states food and drug administration 's database.results:the outcome of classification algorithms showed that improved naive bayes algorithm outperformed naive bayes with accuracy of 90.11% and error of 9.8% in detecting the dechallenge.conclusion:detecting dechallenge for unknown samples are essential for proper prescription . 
 to overcome the issues exposed by naive bayes algorithm , improved naive bayes algorithm can be used to detect dechallenge in terms of higher accuracy and minimal error .