to prevent medical accidents , users must be informed of the cautions written on the package inserts of medicines . these are exclusive legal documents that describe detailed information for each drug , such as the composition , efficacy , dosage , and cautions . however , since the descriptions in package inserts are excessive and there are over 20,000 medicines in japan , it is not easy for healthcare workers to read all documents . as a countermeasure , the utilization of information technology has been focused on in recent years . to develop an information system that alerts healthcare workers at the user interface of an ordering system and an electronic health record system , we need to implement a drug information database as a source of information alerts . in order to utilize the same in information systems , the pharmaceutical and medical devices agency ( pmda)1 has provided standard general markup language ( sgml ) formatted data of package insert information since the year 1999.2 some medical institutions have developed drug databases based on the sgml data to retrieve drug information.3 however , this is not easy since the descriptions in package inserts are too complex and their information poorly structured.48 in this sgml data , most information is directly stored as statements and terms have not been standardized or encoded . since computer systems can not interpret drug information , existing systems can not check prescriptions and administration based on a drug information database . in order to develop such systems , it is necessary to develop a drug information database which has a structure suitable for the purpose . in this paper the reason for analyzing  precautions for application data is that it contains information concerning the usage situation that must be known at the time of prescription . we obtained the sgml data from the pmda web site , utilizing the  yj code  as a key , which is a drug identification code and included in the  standard drug master ( 9/30/2007 )  provided by the medical information system development center ( medis - dc).9 there were 11,685 sgml files . in our target sgml data , since precautionary statements are described in the  precautionsforapplication  field ( as shown in figure 1 ) , we extracted text data from the  item  and  detail  fields in the same . after this extraction , we split each sentence and converted 1 byte characters to 2 bytes . in this study , we removed the sentences described in parentheses , since these can be regarded as additional information . as a result additionally , we applied dependency analysis to the extracted statements via software named cabocha ( v0.53 ; nara institute of science and technology , nara , japan).10 this technique extracts words , parts of speech , segments , and dependency relations between each segment by probabilistic calculation based on defined grammar and dictionary data . in this study , we investigated them by using information such as morphemes , segments , dependency relations , and parts of speech provided by the software . since predicate words are often described at the end of the sentence in japanese , we extracted only headwords in the final segment from the results of dependency analysis . ( must ) was the most commonly occurring word , with a frequency of 83% . it emerges with meanings of  order ,  if we place it after verbs , eg , ( must ) , although there are other order expressions that are weaker , eg , ( desirable ) and that of possibility  
  ( can ) . to extract information concerning the order description , we collected verbs modifying ( must ) in the previous subsection and obtained 415 kinds of headwords in table 2 . in this table , we can see that there are verbs expressing direct orders such as  
  ( use ) , causative orders such as  
  ( make someone use ) , direct interdictions such as  
  ( do not use ) , and causative interdictions such as  
  ( do not make user use ) . since the descriptions of order and interdiction coexist in precautionary statements , therefore , it is necessary to make order / interdiction flags in the database and to provide medical staff with the flags separately by information systems . furthermore , since direct and causative statements coexist , targets to be ordered are not clear . although humans can read them and understand targets , it is difficult to make computers interpret targets . in order to alert to appropriate targets , it is necessary to identify the appropriate targets to be alerted . focusing on these points , we categorized the statements into four categories : direct orders , causative orders , direct interdictions and causative interdictions . in order to summarize information for each category , we tried to employ the word - linking method.11 however , we can not obtain relations between verbs and subjects / objects using this technique because the method extracts only nouns , verbs , and adjectives . to extract such relations , we focused on the case markers ( particles ) appearing at the end of segments that modify the verbs . these are words that indicate the dependency relationship of a certain word to a predicate verb . although we obtained three types of case markers using cabocha :  case markers  general , case markers  collocation ,  and  case markers  citation ,  we used only case markers  general , since it accounted for 90.65% and there were fewer case markers in that type than others . in this study , we focus on the case markers , ( wo ) ,  
  ( ni ) ,  
  ( ga ) ,  
  ( kara ) ,  
  ( de ) , we aggregated ternaries , a verb , a case marker , and the verb modifier , which also appears in the segment containing the case marker . after aggregation , we visualized the ternaries as nodes of a network to find major information in the order statement concerning the four cases , which are direct order , interdiction , causative order , and causative interdiction . in the network , the nodes are collected verbs , case markers , and keywords , and the edges express the connections among them , while their frequencies are expressed as their width . the threshold of appearance frequencies in the data limits the nodes and edges in figures 25 . ( use ) ,  
  ( be careful ) , and ( stop ) ; we can also see statements such as  
  ( use for eye drops ) , though it is an order to avoid , it can be interpreted as an interdiction , since the avoidance is implied in the concept of interdiction . figure 3 shows that the major expressions concern the interdiction of usage . in particular , we can see an interdiction to apply medicines to the affected areas , such as  
  ( do not apply to inflammatory part ) moreover , we obtained statements that indicate an interdiction of mixing drugs , eg , the first is for medical experts and patients , and another type is for medicines . the former gives examples such as  
  ( make patients take the medicine without chewing ) ,  
  ( make patients take the drug after a meal ) , and  
  ( make patients take the medicine with water ) . the latter gives examples such as  
  ( neutralize ) and  
  ( dry ) . they express methods for the preparation and storage of medicines . as causative order statements already mentioned , the subjects of the former include both humans ( pharmacists , nurses and patients ) and medicines . in figure 5 , statements such as  
  ( do not make patients avoid water ) and  
  ( do not regurgitate from syringe ) can be found . based on these results , we found two points concerning precautionary statements that should be considered . since this may lead to the misreading of these statements , they should be described separately . the second point is that we should distinguish two kinds of subjects of statements , namely patients and medical experts . this is important for system checking because the system must know the subjects to whom warnings should be given . since predicate words are often described at the end of the sentence in japanese , we extracted only headwords in the final segment from the results of dependency analysis . ( must ) was the most commonly occurring word , with a frequency of 83% . it emerges with meanings of  order ,  if we place it after verbs , eg , ( must ) , although there are other order expressions that are weaker , eg , ( must ) in the previous subsection and obtained 415 kinds of headwords in table 2 . in this table , we can see that there are verbs expressing direct orders such as  
  ( use ) , causative orders such as  
  ( make someone use ) , direct interdictions such as  
  ( do not use ) , and causative interdictions such as  
  ( do not make user use ) . since the descriptions of order and interdiction coexist in precautionary statements , therefore , it is necessary to make order / interdiction flags in the database and to provide medical staff with the flags separately by information systems . furthermore , since direct and causative statements coexist , targets to be ordered are not clear . although humans can read them and understand targets , it is difficult to make computers interpret targets . in order to alert to appropriate targets , it is necessary to identify the appropriate targets to be alerted . focusing on these points , we categorized the statements into four categories : direct orders , causative orders , direct interdictions and causative interdictions . in order to summarize information for each category , we tried to employ the word - linking method.11 however , we can not obtain relations between verbs and subjects / objects using this technique because the method extracts only nouns , verbs , and adjectives . to extract such relations , we focused on the case markers ( particles ) appearing at the end of segments that modify the verbs . these are words that indicate the dependency relationship of a certain word to a predicate verb . although we obtained three types of case markers using cabocha :  case markers  general , we used only case markers  general , since it accounted for 90.65% and there were fewer case markers in that type than others . in this study , we focus on the case markers , we aggregated ternaries , a verb , a case marker , and the verb modifier , which also appears in the segment containing the case marker . after aggregation , we visualized the ternaries as nodes of a network to find major information in the order statement concerning the four cases , which are direct order , interdiction , causative order , and causative interdiction . in the network , the nodes are collected verbs , case markers , and keywords , and the edges express the connections among them , while their frequencies are expressed as their width . the threshold of appearance frequencies in the data limits the nodes and edges in figures 25 . ( use ) ,  
  ( be careful ) , and ( stop ) ; we can also see statements such as  
  ( use for eye drops ) , ( use after washing ) . it should be noted that the statement though it is an order to avoid , it can be interpreted as an interdiction , since the avoidance is implied in the concept of interdiction . figure 3 shows that the major expressions concern the interdiction of usage . in particular , we can see an interdiction to apply medicines to the affected areas , such as  
  ( do not apply to inflammatory part ) moreover , we obtained statements that indicate an interdiction of mixing drugs , eg , ( do not mix with drugs ) . in figure 4 , we see two types of causative verbs . the first is for medical experts and patients , and another type is for medicines . the former gives examples such as  
  ( make patients take the medicine without chewing ) ,  
  ( make patients take the drug after a meal ) , and the latter gives examples such as  
  ( neutralize ) and  
  ( dry ) . they express methods for the preparation and storage of medicines . as causative order statements already mentioned , the subjects of the former include both humans ( pharmacists , nurses and patients ) and medicines . in figure 5 , statements such as  
  ( do not make patients avoid water ) and  
  ( do not regurgitate from syringe ) can be found . based on these results , we found two points concerning precautionary statements that should be considered . since this may lead to the misreading of these statements , they should be described separately . the second point is that we should distinguish two kinds of subjects of statements , namely patients and medical experts . this is important for system checking because the system must know the subjects to whom warnings should be given . ( use ) ,  
  ( be careful ) , and ( stop ) ; we can also see statements such as  
  ( use for eye drops ) , though it is an order to avoid , it can be interpreted as an interdiction , since the avoidance is implied in the concept of interdiction . figure 3 shows that the major expressions concern the interdiction of usage . in particular , we can see an interdiction to apply medicines to the affected areas , such as  
  ( do not apply to inflammatory part ) moreover , we obtained statements that indicate an interdiction of mixing drugs , eg , the first is for medical experts and patients , and another type is for medicines . the former gives examples such as  
  ( make patients take the medicine without chewing ) ,  
  ( make patients take the drug after a meal ) , and  
  ( make patients take the medicine with water ) . the latter gives examples such as  
  ( neutralize ) and  
  ( dry ) . as causative order statements already mentioned , the subjects of the former include both humans ( pharmacists , nurses and patients ) and medicines . in figure 5 , statements such as  
  ( do not make patients avoid water ) and  
  ( do not regurgitate from syringe ) can be found . based on these results , we found two points concerning precautionary statements that should be considered . since this may lead to the misreading of these statements , they should be described separately . the second point is that we should distinguish two kinds of subjects of statements , namely patients and medical experts . this is important for system checking because the system must know the subjects to whom warnings should be given . in the previous section , we found words that express the time to perform an action eg , orders should be provided to medical staff and patients at the appropriate time . in order to clarify correspondences between timing and orders , we extracted words representing timing from the target data and classified them based on procedures to use drugs . initially , we compiled keywords where the part of speech is an adverbial noun ( table 3 ) . ( when ) as feature words that express the concept of timing , since most segments expressing timings contain these words . the precautionary statements contain mainly two types of descriptions , one of which is that the order is described after the timing expressions in statements eg ,  
  ( be careful at the time of use ) , and another of which where the timing expression appears singly eg ,  
  ( delivery time of medicines ) in the < item >  field of sgml data . in the former case , we extracted keywords that modify the verb expressing the order and whose segments contain the feature words defined above . in the latter case , we extracted keywords that are at the tail of sentences and have feature words . the resultant keywords contained those with similar meanings . to reduce the variety of expressions , we identified such synonymous expressions by hands . we investigated the top 50 extracted words and found six groups :  at the time of delivery ,   at the time of preparation ,   in use , we found that the caution related to  in use  was the largest group . utilizing this classification , we obtained characteristic words that express an order and appear only at specific instances , eg , ( dispose of ) after use and  
  ( store under light interception ) at times of storage . the obtained timing for alerts are vital to prevent accidents in order for the system to specify the time at which to send an alert . in this study , we analyzed precautionary statements in the package inserts of medicines using a text - mining method to develop a drug information database . consequently , we found the major statements used to express orders and interdictions in use . moreover , we obtained points of consideration concerning the targets of orders in the statements and timing of their execution . in this study , although we applied several methods to text data written in japanese , our methods can be employed for any language if a dependency analysis is applied to them . we can also use prepositions in english or other languages instead of particles in japanese language . this suggests that our method is applicable to languages other than japanese . utilizing the knowledge obtained it should contain information such as the actions described in the statement , the flag to express an order or interdiction , the subject to be ordered , and the timing . in the future , this database will be accessed from various systems such as ordering systems and portable information terminals for nurses and patients , and these systems will provide orders and cautions to the appropriate targets at the appropriate time . namely , the systems will provide orders of administration methods to patients and/or nurses just before administration . in future work , we will analyze other information in the package inserts and develop a drug information database based on the results .