according to the society of critical care medicine ( sccm ) , there are approximately five million patients admitted annually to intensive care units ( icus ) in the united states , with average mortality rates ranging from 10% to 29% , which are the highest rates of all the units in a hospital . in addition , the icu has some of the highest rates of medical errors as compared to most clinical settings due to the complexity of care , . with the extensive hemodynamic monitoring and use of multiple measurement technologies , the modern icu generates large volumes of complex and multimodal data .
the data is richer , but the ability to integrate it for effective actions remains difficult .
we have designed and developed an icu clinical decision support system ( cdss ) to improve outcomes in critically ill patients by providing real - time decision support , decreasing medical errors , and minimizing life - threatening events caused by delayed or uninformed medical decisions .
 active knowledge systems which use two or more items of patient data to generate case - specific advice  .
evidence has strongly suggested that cdsss can improve a physician 's decision making performance . for optimal medical decision making
evidence - based medicine is the  conscientious , explicit and judicious use of current best evidence in making decisions about the care of individual patients  .
a cdss is evidence - based if its knowledge base is derived from , and continually reflects , the most up - to - date evidence from the scientific literature and practice - based sources . a generic form of evidence in an evidence - based cdss is the if - then rule .
the rule implies that if an antecedent ( i.e. , a set of conditions ) presents , then an outcome is expected or an action should be taken . in the literature , evidence - based cdsss have provided important risk assessment scores for clinicians , such as prediction of icu survivability ( e.g. , apache ii ) , length of stay , organ failure ( e.g. , sofa ) , neurologic prognosis ( e.g. , glasgow coma score ) , and outcomes after acute coronary syndrome ( e.g. , grace acs model ) .
however , none of the above applications are true cdsss because they are not interactive nor flexible , which are two key features of true decision support systems .
these computer - based icu assistance systems claim to be cdsss because they provide the  statistics  of evidence .
however , it is difficult for clinicians to make a correct decision by recalling all corresponding knowledge based on these statistics in a timely fashion .
they have to search their archives , find the appropriate literature , and interpret relevant evidence ( assuming it is up - to - date ) .
this decision support process is not feasible in the critical care setting , when the luxury of time is rare .
thus , a reliable cdss in an icu should provide not only statistically significant knowledge , but also an interactive user interface that enables clinicians to search for evidence effectively and in real - time . besides being interactive ,
an evidence - based icu cdss need to be flexible . in typical icu systems that claim to be cdsss ,
researchers define expected if - then rules ( i.e. , with conditions and outcomes or actions ) given certain clinical problems , validate the rules ( i.e. , via human trial , lab experiment , or computer simulation ) , and form new evidence if the rule is statistically significant ( e.g. , \documentclass[12pt]{minimal } 
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 } { } $ p<0.05$\end{document } ) .
for example , a clinician can select an appropriate antimicrobial drug for a septic patient when the pathogenic organism has specific hemodynamic and biochemical markers .
however , some patients lack clear - cut evidence for the presence of an infection and/or the type of infecting organism , which makes the decision to treat with an antimicrobial drug experience - based instead of evidence - based .
the clinician still needs to make the same decision about antimicrobial drug prescription with incomplete information , and then passively assess the prognosis .
such a process introduces human bias that deviates from the original design of the cdss .
clinicians face this challenge on a daily basis for every patient in the icu due to heterogeneous conditions .
therefore , a flexible icu cdss is needed to allow clinicians to customize conditions to better describe a patient 's immediate status ( i.e. , personalized ) , instead of referring to fixed evidence formed from different clinical situations .
in addition to the two key features mentioned above , a powerful cdss relies on a sufficient and representative database of patient icu stays .
although the bedside monitor can generate large amounts of data from each patient as compared to other care settings , the number of unique patients in a standard icu cdss database is typically small .
thus , the accuracy of new decision support evidence is limited by the diversity of phenotypes contained in a small number of patients . even though some studies contain large numbers of patients , the evidence is usually mined from the entire cohort without clinical categorization .
ideally , before applying data modeling analysis , a cdss should first extract a cohort of patients who have similar medical histories and situations , and reveal the sample size as a reference when delivering new evidence back to clinicians .
clinicians can judge when the evidence is mined from a sufficient and representative dataset . to our knowledge , to date
, no true cdsss have been developed for the icu . to address the aforementioned challenges
icuarm has an interactive graphical user interface ( gui ) that allows clinicians to mine decision support evidence in a flexible way .
we constructed association rules to perform evidence - based data mining from a database with more than 40,000 icu stays of \documentclass[12pt]{minimal } 
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 } { } $ 30,000+{\rm patients}$\end{document}. we will report icuarm as the following structure .
first , we provide a short description of data source in section ii - a .
then we describe the detailed data mining approach and the system user interface in sections ii - b and ii - c respectively .
next we present results , evaluation and discussion of two case studies in section iii .
the data in the icuarm is imported from the multi - parameter intelligent monitoring in intensive care ii ( mimic - ii ) database .
mimic - ii is a publicly accessible icu data repository containing records of over 40,000 icu stays in which 32,000 are adult \documentclass[12pt]{minimal } 
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 } { } $ ( { > } { \rm 15}~{\rm yrs})$\end{document } records and 8,000 are neonatal \documentclass[12pt]{minimal } 
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 } { } $ ( { < } { \rm 2}~{\rm yrs})$\end{document } records . the data in mimic - ii can be categorized into two major categories : clinical data and physiological data .
the clinical data is collected from mimic - ii 's icu information systems and hospital electronic health record systems .
the high - resolution physiological data consists of time series waveforms and time series measurements from bedside monitors .
the data mining process in this study only includes clinical data . in the near future
we further divide the mimic - ii clinical data into two groups of categories : basic and event - based .
the basic categories include data that remain unchanged during one icu stay ( e.g. , patient demographics and pre - existing comorbidities ) .
the event - based categories contain data collected at multiple time points within an icu stay , including laboratory tests ( e.g. , blood chemistries , complete blood counts ) , medication events ( e.g. , insulin , heparin ) , fluid balance ( e.g. , urine output ) , and nurse - verified chart measurements ( e.g. , blood pressure , heart rate ) .
values in event - based categories are processed to generate mean , minimum , maximum , and standard deviation during an icu stay .
we tabularized the imported mimic - ii clinical data in table i. 
 table idescription of mimic - ii clinical datacategory ( mapping)eventmeasure examplesno .
of available variablesbasic ( 1-to-1)icu general datanumber of icu stays , stay sequence , first / last icu day flag , stay in / out date , stay length ( min ) , stay death flag , care unit of the first / last icu day , saps score , sofa.42demographicsgender , age , ethnicity , religion , date of birth / death , death flag , marital status , weight , height , admission type.14comorbiditiescongestive heart failure , hypertension , cardiac arrhythmias , pulmonary circulation , peripheral vascular , paralysis , neurological disorder , chronic pulmonary , cancer , aids , diabetes.32events ( 1-to - n)3medicationspropofol , insulin , fentanyl , heparin , neosynephrine - k , levophed - k , midazolam , furosemide , lorazepam , labetolol , eptifibatide.4063fluid balancesurine out , d10w , promote with fiber , replete with fiber , lactated ringer s , free water bolus , gastric , nasogastric.6,809laboratory testshematocrit of blood , potassium in serum or plasma , creatinine in serum or plasma , urea nitrogen in serum or plasma , hemoglobin in blood , ph of blood.714nurse - verified chartingheart rate , heart rhythm , blood pressure , noninvasive blood pressure , central venous pressure , 5ao2 , arterial ph , arterial paco2 , arterial pao2 , arterial co2 , spo2 , respiratory rate.4,781simplified acute physiology score sequential organ failure assessment event - based categories that include event duration 
   simplified acute physiology score sequential organ failure assessment event - based categories that include event duration after importing data from mimic - ii , a sophisticated mining process is required to unearth meaningful associations from such a voluminous dataset .
association rule mining ( arm ) is a method to reveal meaningful relations between variables in databases .
first introduced the concept of arm to extract regularities between products in large - scale warehouse databases . in healthcare
, arm has been widely adopted in applications such as heart disease prediction , , healthcare auditing , , and neurological diagnosis , . however , to our understanding , there is no published cdss reporting the use of arm in the icu .
rules in arm are in the form of \documentclass[12pt]{minimal } 
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 } { } $ y$\end{document } are called antecedent and consequent , respectively . in its original marketing analysis context , the rule \documentclass[12pt]{minimal } 

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 } { } $ y$\end{document}. in mimic - ii , one patient may be associated with one or more hospital stays ; and one hospital stay may be associated with one or more icu stays .
therefore , the most basic data piece in our mining process is the icu stay .
therefore , in our icu data mining process , a rule \documentclass[12pt]{minimal } 
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the icu stays are analogous to transactions in the traditional marketing arm in which a customer may have multiple market visits , and a market visit may have multiple transactions .
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 } { } $ y$\end{document } are itemsets that consist of one or more item(s ) .
an item is composed of a variable with a corresponding value or a range of values .
an item can be numerical or categorical depending on the data type of the variable .
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 } { } $ y$\end{document } can consist of one or a combination of items , the associations are not necessarily one - to - one .
two important metrics support and confidence  quantify the frequency and level of association of a rule .
we modified these two metrics from their conventional forms to fit our icu clinical mining .
first , the support of an association rule is defined as:\documentclass[12pt]{minimal } 
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 } { } $ $ supp(x\rightarrow y)={{count(x\cup y)}\over{{count}((x_{v}\ne\phi)\cup ( y_{v}\ne\phi))}}\eqno{\hbox{(1)}}$$\end{document } where \documentclass[12pt]{minimal } 
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 } { } $ x=\hbox{``}heartratemax>80,\hbox{''}x_{v}\ne\phi$\end{document } refers to all icu stays in which heartratemax has been assigned a value ( i.e. , no missing data ) .
count(a ) returns the number of icu stays that contain \documentclass[12pt]{minimal } 
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 } { } $ y$\end{document}. this is critical in icu data mining because clinical data is usually recorded when the patient is presenting a specific condition or undergoing a specific treatment .
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 } { } $ y$\end{document } helps extract stays that are under similar clinical conditions .
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 } { } $ y$\end{document}. the support ranges from 0 to 1 .
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 } { } $ $ conf(x\rightarrow y)={{count(x\cup y)}\over{count({\rm x}\cup ( y_{v}\ne\phi))}}\eqno{\hbox{(2)}}$$\end{document } in all icu stays that have no missing value in variables of \documentclass[12pt]{minimal } 
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 } { } $ conf_{min}$\end{document}. because the second step is straightforward , most of the research focus is on the first step .
since the first algorithm was introduced in the original report of arm , new algorithms have been proposed to improve the efficiency of the generation of frequent itemsets . among these algorithms ,
let \documentclass[12pt]{minimal } 
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in the first iteration , the algorithm starts by counting the occurrence of 1-itemset candidates that contain only one item .
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 } { } $ supp_{min}$\end{document } are pruned out and the remaining ones are called frequent 1-itemsets . in the following iterations ( i.e. , \documentclass[12pt]{minimal } 
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 } { } $ supp_{min}$\end{document}. the iteration continues until no more candidates or frequent itemsets can be found .
the pseudo - code of the apriori algorithm presented in   is given as follows : 

  the gencandidate in the apriori algorithm is the candidate itemset generation algorithm that is given as follows : 

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 } { } $ x\rightarrow y$\end{document } yields a higher confidence than its counter case .
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 } { } $ $ { impo}(x\rightarrow y)={{conf(x\rightarrow y)}\over{conf(\overline x\rightarrow y)}}\eqno{\hbox{(3)}}$$\end{document } the importance metric ranges from 0 to \documentclass[12pt]{minimal } 
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 } { } $ \infty$\end{document}. a rule of importance less than 1 means that the antecedent predicts the consequent worse than the counter case of the antecedent .
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 } { } $ { > } { 1}$\end{document}. to avoid the rules with importance close to 1 ( i.e. , 0.9 and 1.1 ) due to random chance , a more strict and higher threshold for importance are used to ensure statistical significance . in this study ,
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 } { } $ y$\end{document } may be associated with different antecedents from different rules .
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 } { } $ $ domi(x\rightarrow y)={{count(y\cup x)}\over{count({\rm y}\cup ( x_{v}\ne\phi))}}\eqno{\hbox{(4)}}$$\end{document } similar to ( 2 ) , the dominance ranges from 0 to 1 .
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 } { } $ y\rightarrow x$\end{document } because the rule can be viewed as the ratio of icu stays that match all items in \documentclass[12pt]{minimal } 
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 } { } $ x$\end{document}. because the antecedent of a rule can consist of multiple items , we may also need to determine how a new item affects a rule 's confidence when we include the item in the rule 's original antecedent ( i.e. , \documentclass[12pt]{minimal } 
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 } { } $ x\cup i_{new}\rightarrow y$\end{document } ) .
evaluating effects of new clinical items would be helpful , for example , in making decisions about medication or treatment combinations . additionally , it is not necessary to always pursue positive or negative effects ; rather , it depends on the clinical situation .
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 } { } $ x$\end{document } , we may not only be interested in drugs that may decrease mortality , but also in those that may increase mortality . in this study , we use the following equation to measure the effect of adding a new antecedent item on a rule 's confidence:\documentclass[12pt]{minimal } 
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 } { } $ $ effe(i_{new}\vert x\rightarrow y)=conf(x\cup i_{new}\rightarrow y)conf(x\rightarrow y)\eqno{\hbox{(5)}}$$\end{document } the range of the effect metric is from \documentclass[12pt]{minimal } 
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 } { } $ { -}{1}$\end{document } to 1 . unlike the support , confidence , importance , and dominance that are all rule - wise metrics , effect
the icuarm features a user interface that allows real - time association rule mining in the icu .
the interface enables the user to input real - time patient clinical scenarios , extract confident association rules from the database , and display the rules . the icuarm usage flow is shown in fig .
1 . as depicted , the icuarm system consists of three main windows : ( 1 ) a rule mining window , ( 2 ) an effect browsing window , and ( 3 ) a new item window .
the system consists of three main windows , including a rule mining window ( a ) , an effect browsing window ( b ) , and a new item window ( c ) .
the system synchronizes with three sources , including the mimic - ii database , item bank that stores all created items , and rule bank that stores all pre - existing rules . 
 
the system consists of three main windows , including a rule mining window ( a ) , an effect browsing window ( b ) , and a new item window ( c ) .
the system synchronizes with three sources , including the mimic - ii database , item bank that stores all created items , and rule bank that stores all pre - existing rules .
2 ) that enables clinicians to extract association rules based on customized antecedents and consequents .
the first data source of icuarm is the rule bank which stores rules that were previously mined .
the second data source is the item bank that stores all pre - existing items .
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 } { } $ conf_{min}=0\%$\end{document}. the user can apply several inputs to prune out infrequent and/or unconfident rules by increasing \documentclass[12pt]{minimal } 
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 } { } $ { conf}_{min}$\end{document } , respectively .
because antecedents and consequents of the displayed rules may contain multiple items , the user can specify the length ( i.e. , the number of items ) of processed antecedents and/or consequents .
in addition , the rules can be sorted by one of the four rule - wise metrics ( i.e. , support , confidence , importance , and dominance ) .
furthermore , the user can export all raw rules or processed rules in comma - separated values ( csv ) format , which can be exported into microsoft excel or other statistical analysis tools ( e.g. , spss ) for future analysis . 
 fig .
users can construct antecedents and consequents of interest by selecting items in the antecedent panel and the consequent panel , respectively .
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 } { } $ { \rm conf}_{min}$\end{document } , and the sorting type .
users can access the new item window and effect browsing window via the bottom two buttons . 
  the rule mining window .
users can construct antecedents and consequents of interest by selecting items in the antecedent panel and the consequent panel , respectively .
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 } { } $ { \rm conf}_{min}$\end{document } , and the sorting type .
users can access the new item window and effect browsing window via the bottom two buttons .
3 ) that enables clinicians to browse item - wise effects after the rules are extracted from the rule bank .
when given a target consequent , this interface starts by displaying all rules that contain only one item in their antecedents with their corresponding four rule - wise metrics .
once a first - item has been selected in the antecedent , the browser lists all possible second - items with their corresponding effects ( i.e. , the confidence changes ) .
the browsing continues until no more potential items can be selected in the antecedent . 

the antecedent with selected items is shown on the left with the current rule measures .
users can keep selecting possible items from the right until no more potential items are available .
the antecedent with selected items is shown on the left with the current rule measures .
users can keep selecting possible items from the right until no more potential items are available .
4 ) that enables clinicians to create new items . as mentioned in the rule mining window , the user can select items to construct customized antecedents and consequents .
however , the user may not always find items of interest that have been created in the system .
therefore , the new item window allows the user to construct new items by selecting a category , choosing a variable ( i.e. , the first component of the item ) under the category , and assigning a value or a range of values ( i.e. , the second component of the item ) to the variable .
if the chosen variable is event - based , the user can also assign a value of mean , minimum , maximum , and standard deviation ( as we mentioned in section ii - a ) .
once the user submits new items , the system stores them in the item bank , accesses the mimic - ii database , and generates new rules .
the efficiency of rule generation depends on the computation power of the running pc and can be expedited by parallel computing .
the current system can generate from 52 rules per minute on a single - core processor to 434 rules per minute on a 12-core processor . upon creation of new rules ,
afterwards , users can retrieve the rules in the rule bank via the rule mining window without waiting for duplicated rule generation . as the number of stored rules keeps growing
create new item panel , users can create new items by selecting a variable under a category and assigning a value or a range of values .
the pending new items are listed in the new items panel . by clicking the submit button , the system stores the new items and generates all corresponding rules .
users can return to the rule mining window by clicking the mining button . 
  the new item window .
in the create new item panel , users can create new items by selecting a variable under a category and assigning a value or a range of values .
the pending new items are listed in the new items panel . by clicking the submit button , the system stores the new items and generates all corresponding rules .
the length of stay ( los ) is a significant icu outcome that is associated with severe organ failure and high resource consumption , .
evidence has shown that patients with prolonged ( i.e. , longer than 3 days ) icu stays have a considerably increased icu , hospital , and long - term mortality .
however , survival is typically estimated on a long - term basis ( e.g. , 1-yr or 2-yrs survival ) that is not applicable to the short - term icu prediction .
therefore , in this case study , we employed the icuarm to generate association rules between pre - existing comorbidities and prolonged icu stays .
table ii lists the four metrics of rules for 12 possible pre - existing comorbidities .
the rule with hypothyroidism ( hyp ) as the comorbidity was ignored because its rule had importance less than 1 .
congestive heart failure ( chf ) had the highest support ( 8.6% ) , which means that this rule was applicable to the highest portion of the icu stays . additionally , according to the dominance metric , chf dominated the prolonged icu stays by 22.4% , which was also the highest . 

table iipre - existing comorbidities vs. prolonged icu stayabbr.comorbiditysupp ( % ) conf ( % ) impodomi ( % ) coacoagulopathy2.854.11.47.4chfcongestive heart failure8.649.91.422.4caacardiac arrhythmias7.346.01.319.1refrenal failure2.444.61.26.3lidliver disease1.944.51.25.0obeobesity0.643.01.12.4chpchronic pulmonary5.641.51.114.8deadeficiency anemia3.640.61.19.4diadiabetes6.938.91.118.6cancancer4.537.11.012.2abualcohol / drug abuse2.036.91.05.3hyphypothyroidism2.335.00.9 * 6.0*rule is ignored because the importance is less than 1 . 
 
* rule is ignored because the importance is less than 1 . in this case study ,
the possibility of prolonged icu stay can be predicted by the confidence of a rule given the comorbidities in the antecedent . according to the importance rules
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 } { } $ { \geq}{1}$\end{document } ) shown in table ii , coagulopathy ( coa ) is associated with the highest possibility of prolonged icu stay ( 54.1% ) , whereas alcohol / drug abuse ( abu ) is associated with the lowest possibility ( 36.9%).some comorbidities tend to be associated with shorter icu stays , especially in younger population . for example , young patients with abu usually admit for short ( i.e. , \documentclass[12pt]{minimal } 
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 } { } $ { < } { \rm 24}~{\rm hrs}$\end{document } ) icu stays because of acute alcohol intoxication ( e.g. , seizures / delirium tremens ) or drug overdose / intoxication ( e.g. , respiratory monitoring after an opiate or benzodiazepine overdose ) . the association rules of the eight age - gender populations ( i.e. , age \documentclass[12pt]{minimal } 
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 } { } $ { < } { 2}$\end{document } , 1530 , 3050 , \documentclass[12pt]{minimal } 
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 } { } $ { > } { 50}$\end{document } years , and gender male and female ) were also generated .
5(a ) shows that females aged over 50 had the highest possibility of prolonged icu stay ( 38.8% ) , and males over 50 years had the second highest ( 38.2% ) .
interestingly , the pediatric population had the third and fourth highest possibility of prolonged icu stay ( female : 37.6% , male : 38.1% ) .
this may be a reflection of the fact that most of the children within the mimic - ii database are neonates in the neonatal icu , where premature infants tend to have prolonged stays ( up to months ) . 

the three values of each bar are the three measures of the association rules , including support ( top ) , importance ( middle ) , and dominance ( bottom ) .
( b ) the effects ( i.e. , changes in possibility of prolonged icu stay ) of the first - item comorbidities on prolonged icu stay possibility in females aged over 50 years .
( c ) the effects of the second - item comorbidities on prolonged icu stay possibility in females aged over 50 years who also have coagulopathy . 
 
some comorbidities tend to be associated with shorter icu stays , especially in younger population .
for example , young patients with abu usually admit for short ( i.e. , \documentclass[12pt]{minimal } 
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 } { } $ { < } { \rm 24}~{\rm hrs}$\end{document } ) icu stays because of acute alcohol intoxication ( e.g. , seizures / delirium tremens ) or drug overdose / intoxication ( e.g. , respiratory monitoring after an opiate or benzodiazepine overdose ) . (
the three values of each bar are the three measures of the association rules , including support ( top ) , importance ( middle ) , and dominance ( bottom ) .
( b ) the effects ( i.e. , changes in possibility of prolonged icu stay ) of the first - item comorbidities on prolonged icu stay possibility in females aged over 50 years .
( c ) the effects of the second - item comorbidities on prolonged icu stay possibility in females aged over 50 years who also have coagulopathy .
the icuarm 's effect browsing window was used to investigate the effect of different combinations of pre - existing comorbidities in different populations on the possibility of prolonged icu stay .
we focused on females aged over 50 because of their highest possibility of prolonged icu stay .
5(b ) shows all rules of the 11 first - item comorbidities in this population .
coagulopathy ( coa ) was still associated with the highest possibility ( 58.6% ) of prolonged icu stay .
in addition , females over 50 years who had alcohol and/or drug abuse showed an increased possibility ( 44.1% ) even though this comorbidity did not have a high risk in the general population .
we continued to investigate the effects ( i.e. , changes of possibility of prolonged icu stay ) of possible second - item comorbidities in females aged over 50 years who also had coagulopathy .
5(c ) , there were 10 possible second - item comorbidities that were important ( importance \documentclass[12pt]{minimal } 
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 } { } $ { \geq}{1}$\end{document } ) . among them , six comorbidities increased the possibility , with deficiency anemia ( dea ) resulting in the highest rate of prolonged icu stay ( 64.4% ) .
clinicians can continue the effect browsing process by adding other comorbidity combinations based on a patient 's status at admission . after evaluating rules based on their support and confidence values
rules have lower importance values for more general cases with fewer items ( in either the antecedent or consequent ) , and have higher importance values for more specific cases with more items .
5(a)(c ) , we can observe that the importance values increase as more items are added to the antecedents .
so even if , theoretically , the value can go to infinity , in reality , it is more around 1.11.4 . in this case study , we have shown the basic usability of icuarm to assess associations between pre - existing comorbidities and prolonged icu stays , especially in females aged over 50 years .
clinicians can construct different combinations of age , gender , and pre - existing comorbidities to determine a baseline prolonged icu stay possibility of a patient at the time of icu admission , even prior to diagnosis . by estimating the possibility of prolonged icu stay , an icu team can efficiently plan ahead for the intensive care resource allocation such as staffing , laboratory , and radiology .
this prediction also provides a risk reference to assess how certain interventions will affect los .
one has a coagulopathy ( e.g. , disseminated intravascular coagulation ) and the other has an acute coronary syndrome . by using icuarm , the clinician and icu team could accurately plan for needed resources for the former patient , predict outcomes , and improve management for this type of high risk icu patient .
arm has been adopted in several pharmacovigilance studies , such as investigating multi - item adverse drug reactions . however ,
to our knowledge , no cdsss have adopted arm for finding associations between medication usage and icu outcomes .
therefore , in our second case study , by using icuarm , we investigated the associations between prolonged icu stays and medication usage in addition to patient demographics and pre - existing comorbidities .
we first mined the association rules of two commonly used anti - hypertensive drugs in icus : diltiazem ( dil ) and labetalol ( lab ) .
we selected males and females over 50 years because they had the highest prolonged icu possibility according to our previous case study .
the associations on the drugs with a pre - existing comorbidity of congestive heart failure ( chf ) were also investigated . as shown in table iii , in patients over 50 years without chf ,
dil is associated with higher possibility compared to lab in both females and males . however , these two drugs had different effects on patients with chf . for females over 50 years with chf ,
the use of dil increased the possibility of prolonged icu stays to 83.4% compared to females over 50 without chf ( 73.6% ) , whereas lab had nearly no change ( 62.3% vs. 61.7% ) .
in contrast , for the same clinical situation , the use of lab actually increased the possibility in males over 50 years with chf to 87.1% compared to those without chf ( 62.8% ) , whereas dil had almost no effect ( 74.7% vs. 76.0% ) .
therefore , for patients over 50 years with a comorbidity of chf , we may choose lab for females and dil for males . 
 table iiimedication usage vs. prolonged icu stay ( \documentclass[12pt]{minimal } 
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 } { } $ { > } { \rm 50}$\end{document } years old)probability of prolonged icu stay ( % ) comorbiditygenderlabdilepivasepi+vasno chffemale62.373.667.664.767.6male62.874.761.871.474.0has chffemale61.783.484.582.084.2male87.176.068.185.287.8 
  in addition to the hypertensive conditions ,
icu clinicians often have a choice between pharmacologic agents in an acute episode of cardiopulmonary arrest .
epinephrine ( epi ) and vasopressin ( vas ) are two common drugs used in the management of ventricular fibrillation and pulseless electrical activity .
we applied icuarm to explore the associations between these two drugs and prolonged icu stays .
suggested that the combination of epi and vas did not improve outcome ( i.e. , survival to hospital discharge , good neurologic recovery , and 1-year survival ) during advanced cardiac life support for out - of - hospital cardiac arrest .
however , evidence was still insufficient to make prognosis on short - term icu stays .
therefore , by utilizing icuarm , the association between icu los and a combination of epi and vas was also evaluated compared to epi or vas alone . according to the result shown in table iii , females over 50 years without chf had slightly lower possibility of prolonged icu stay with vas compared to epi ( 64.7% vs. 67.6% ) ; in contrast , males over 50 without chf had lower chance of prolonged icu stay with epi compared to vas ( 61.8% vs. 71.4% ) .
these associations all increased on patients over 50 who also had chf , but the epi increased the possibility most on females ( 84.5% ) compared to those without chf ( 67.6% ) .
furthermore , for the combination of epi and vas , the change of the possibility was not considerably different compared to epi or vas alone .
this partially supported the finding of   although we focused on the short term icu outcome .
this case study demonstrated that icuarm could help guide the clinician to select correct medication for similar clinical situations but different patient populations in the pre - planning phase .
the entire mining process requires no more than one minute , promising a nearly real - time and easy - to - access bedside consulting tool .
evidence - based real - time decision - making for critically ill patients in the icu has become more challenging because the volume and complexity of the data have been increasing over the years .
thus , to assist clinicians in making optimal decisions , there is a critical need to apply modern information technology and advanced data analytics to extract information from heterogeneous clinical data . in this study
, we researched and developed a real - time clinical decision support system icuarm to assist clinicians in generating quantitative and real - time decision support rules for the icu based on a large icu patient database mimic - ii .
we adopted the  support  and the  confidence  metrics suitable for icu clinical application from conventional association rule mining .
in addition , we defined and developed two new rule - wise metrics  importance  and  dominance  and one item - wise metric  effect .
 we developed an interactive and easy - to - use graphical user interface that enables clinicians to perform flexible data mining in real - time for personalized decision - making .
we tested icuarm on two cases investigating the associations between prolonged icu stays and patient demographics , pre - existing comorbidities , and medication usage .
our results not only reinforced the current decision - making evidence , but also revealed new knowledge by predicting characteristics of a prolonged icu stay .
first , besides basic patient demographics , pre - existing comorbidity data , and medication usage , we will emulate more categories such as nurse - verified chart events , laboratory tests , and fluid balance records etc . to better assist icu clinicians in making critical decisions .
second , as mentioned in section ii - a , we aim to include continuous physiological data with corresponding time stamps from the mimic - ii database to perform temporal association rule mining .
third , the current mining process with the apriori algorithm requires clinicians to manually specify variables of interest and cut - points ( for numerical variables ) in the items of antecedents and consequents .
we will develop automatic feature selection , such as the supervised mrmr method   or the unsupervised mcfs method , and discretization for more objective item construction .
finally , in addition to the five evaluation metrics , we will provide a more comprehensive clinical evaluation by including other data centric rule metrics .
 the rapid development of biomedical monitoring technologies has enabled modern intensive care units ( icus ) to gather vast amounts of multimodal measurement data about their patients . however , processing large volumes of complex data in real - time has become a big challenge . 
 together with icu physicians , we have designed and developed an icu clinical decision support system icuarm based on associate rule mining ( arm ) , and a publicly available research database mimic - ii ( multi - parameter intelligent monitoring in intensive care ii ) that contains more than 40,000 icu records for 30,000+patients . 
 icuarm is constructed with multiple association rules and an easy - to - use graphical user interface ( gui ) for care providers to perform real - time data and information mining in the icu setting . to validate icuarm , 
 we have investigated the associations between patients ' conditions such as comorbidities , demographics , and medications and their icu outcomes such as icu length of stay . 
 coagulopathy surfaced as the most dangerous co - morbidity that leads to the highest possibility ( 54.1% ) of prolonged icu stay . 
 in addition , women who are older than 50 years have the highest possibility ( 38.8% ) of prolonged icu stay . for clinical conditions treatable with multiple drugs , icuarm suggests that medication choice can be optimized based on patient - specific characteristics . 
 overall , icuarm can provide valuable insights for icu physicians to tailor a patient 's treatment based on his or her clinical status in real time . 
Introduction
Methods and Procedures
Results and Discussion
Conclusion