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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Analysis on descriptions of precautionary statements in package inserts of medicines</title><author><name sortKey="Nabeta, Keita" sort="Nabeta, Keita" uniqKey="Nabeta K" first="Keita" last="Nabeta">Keita Nabeta</name><affiliation><nlm:aff id="af1-dhps-4-019">Graduate School of Engineering and Science, Shibaura Institute of Technology, Toyosu 3-7-5, Koto-ku, Tokyo, 135-8548 Japan</nlm:aff></affiliation></author><author><name sortKey="Kimura, Masaomi" sort="Kimura, Masaomi" uniqKey="Kimura M" first="Masaomi" last="Kimura">Masaomi Kimura</name><affiliation><nlm:aff id="af2-dhps-4-019">Faculty of Engineering, Shibaura Institute of Technology, Toyosu 3-7-5, Koto-ku, Tokyo, 135-8548 Japan</nlm:aff></affiliation></author><author><name sortKey="Ohkura, Michiko" sort="Ohkura, Michiko" uniqKey="Ohkura M" first="Michiko" last="Ohkura">Michiko Ohkura</name><affiliation><nlm:aff id="af2-dhps-4-019">Faculty of Engineering, Shibaura Institute of Technology, Toyosu 3-7-5, Koto-ku, Tokyo, 135-8548 Japan</nlm:aff></affiliation></author><author><name sortKey="Tsuchiya, Fumito" sort="Tsuchiya, Fumito" uniqKey="Tsuchiya F" first="Fumito" last="Tsuchiya">Fumito Tsuchiya</name><affiliation><nlm:aff id="af3-dhps-4-019">School of Pharmacy, International University of Health and Welfare, Minami-Aoyama 1-24-1, Minato-ku, Tokyo, 107-0062 Japan</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22399866</idno><idno type="pmc">3295621</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3295621</idno><idno type="RBID">PMC:3295621</idno><idno type="doi">10.2147/DHPS.S25921</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000184</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000184</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Analysis on descriptions of precautionary statements in package inserts of medicines</title><author><name sortKey="Nabeta, Keita" sort="Nabeta, Keita" uniqKey="Nabeta K" first="Keita" last="Nabeta">Keita Nabeta</name><affiliation><nlm:aff id="af1-dhps-4-019">Graduate School of Engineering and Science, Shibaura Institute of Technology, Toyosu 3-7-5, Koto-ku, Tokyo, 135-8548 Japan</nlm:aff></affiliation></author><author><name sortKey="Kimura, Masaomi" sort="Kimura, Masaomi" uniqKey="Kimura M" first="Masaomi" last="Kimura">Masaomi Kimura</name><affiliation><nlm:aff id="af2-dhps-4-019">Faculty of Engineering, Shibaura Institute of Technology, Toyosu 3-7-5, Koto-ku, Tokyo, 135-8548 Japan</nlm:aff></affiliation></author><author><name sortKey="Ohkura, Michiko" sort="Ohkura, Michiko" uniqKey="Ohkura M" first="Michiko" last="Ohkura">Michiko Ohkura</name><affiliation><nlm:aff id="af2-dhps-4-019">Faculty of Engineering, Shibaura Institute of Technology, Toyosu 3-7-5, Koto-ku, Tokyo, 135-8548 Japan</nlm:aff></affiliation></author><author><name sortKey="Tsuchiya, Fumito" sort="Tsuchiya, Fumito" uniqKey="Tsuchiya F" first="Fumito" last="Tsuchiya">Fumito Tsuchiya</name><affiliation><nlm:aff id="af3-dhps-4-019">School of Pharmacy, International University of Health and Welfare, Minami-Aoyama 1-24-1, Minato-ku, Tokyo, 107-0062 Japan</nlm:aff></affiliation></author></analytic><series><title level="j">Drug, Healthcare and Patient Safety</title><idno type="eISSN">1179-1365</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>To prevent medical accidents, users must be informed of the cautions written in medical package inserts. To realize countermeasures by utilizing information systems, we must also implement a drug information database. However, this is not easy to develop, since the descriptions in package inserts are too complex and their information poorly structured. It is necessary to analyze package insert information and propose a data structure.</p></sec><sec><title>Methods</title><p>We analyzed the descriptions of ‘precautions for application’ in package inserts via text mining methods. In order to summarize statements, we applied dependency analysis to statements and visualized their relations between predicate words and other words. Furthermore, we extracted words representing timing to execute the order.</p></sec><sec><title>Results</title><p>We found that there are four types of statements: direct orders such as “
<inline-graphic xlink:href="dhps-4-019f6.jpg"></inline-graphic>” (use), causative orders such as “
<inline-graphic xlink:href="dhps-4-019f7.jpg"></inline-graphic>” (make someone use), direct interdictions such as “
<inline-graphic xlink:href="dhps-4-019f8.jpg"></inline-graphic>” (do not use), and causative interdictions such as “
<inline-graphic xlink:href="dhps-4-019f9.jpg"></inline-graphic>” (do not make user use). As for words representing timing, we extracted six groups: ”at the time of delivery,” “at the time of preparation,” “in use,” “after use,” and “at the time of storage.” From these results, we obtained points of consideration concerning the subjects of orders in the statements and timing of their execution.</p></sec><sec><title>Conclusion</title><p>From the obtained knowledge, we can define the information structure used to describe the precautionary statement. 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.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Orii, T" uniqKey="Orii T">T Orii</name></author><author><name sortKey="Yamazaki, M" uniqKey="Yamazaki M">M Yamazaki</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fujimoto, K" uniqKey="Fujimoto K">K Fujimoto</name></author><author><name sortKey="Kuwata, S" uniqKey="Kuwata S">S Kuwata</name></author><author><name sortKey="Matsumura, Y" uniqKey="Matsumura Y">Y Matsumura</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Otsuki, C" uniqKey="Otsuki C">C Otsuki</name></author><author><name sortKey="Ota, T" uniqKey="Ota T">T Ota</name></author><author><name sortKey="Sunagane, N" uniqKey="Sunagane N">N Sunagane</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Togashi, H" uniqKey="Togashi H">H Togashi</name></author><author><name sortKey="Kuribara, M" uniqKey="Kuribara M">M Kuribara</name></author><author><name sortKey="Orii, T" uniqKey="Orii T">T Orii</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hamada, M" uniqKey="Hamada M">M Hamada</name></author><author><name sortKey="Hirota, M" uniqKey="Hirota M">M Hirota</name></author><author><name sortKey="Kurata, K" uniqKey="Kurata K">K Kurata</name></author><author><name sortKey="Dobashi, A" uniqKey="Dobashi A">A Dobashi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Nabeta, K" uniqKey="Nabeta K">K Nabeta</name></author><author><name sortKey="Kimura, M" uniqKey="Kimura M">M Kimura</name></author><author><name sortKey="Ohkura, M" uniqKey="Ohkura M">M Ohkura</name></author><author><name sortKey="Tsuchiya, F" uniqKey="Tsuchiya F">F Tsuchiya</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kimura, M" uniqKey="Kimura M">M Kimura</name></author><author><name sortKey="Okada, K" uniqKey="Okada K">K Okada</name></author><author><name sortKey="Nabeta, K" uniqKey="Nabeta K">K Nabeta</name></author><author><name sortKey="Ohkura, M" uniqKey="Ohkura M">M Ohkura</name></author><author><name sortKey="Tsuchiya, F" uniqKey="Tsuchiya F">F Tsuchiya</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Kudo, T" uniqKey="Kudo T">T Kudo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kimura, M" uniqKey="Kimura M">M Kimura</name></author><author><name sortKey="Furukawa, H" uniqKey="Furukawa H">H Furukawa</name></author><author><name sortKey="Tsukamoto, H" uniqKey="Tsukamoto H">H Tsukamoto</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Drug Healthc Patient Saf</journal-id><journal-id journal-id-type="iso-abbrev">Drug Healthc Patient Saf</journal-id><journal-title-group><journal-title>Drug, Healthcare and Patient Safety</journal-title></journal-title-group><issn pub-type="epub">1179-1365</issn><publisher><publisher-name>Dove Medical Press</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22399866</article-id><article-id pub-id-type="pmc">3295621</article-id><article-id pub-id-type="doi">10.2147/DHPS.S25921</article-id><article-id pub-id-type="publisher-id">dhps-4-019</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Research</subject></subj-group></article-categories><title-group><article-title>Analysis on descriptions of precautionary statements in package inserts of medicines</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Nabeta</surname><given-names>Keita</given-names></name><xref ref-type="aff" rid="af1-dhps-4-019">1</xref><xref ref-type="corresp" rid="c1-dhps-4-019"></xref></contrib><contrib contrib-type="author"><name><surname>Kimura</surname><given-names>Masaomi</given-names></name><xref ref-type="aff" rid="af2-dhps-4-019">2</xref></contrib><contrib contrib-type="author"><name><surname>Ohkura</surname><given-names>Michiko</given-names></name><xref ref-type="aff" rid="af2-dhps-4-019">2</xref></contrib><contrib contrib-type="author"><name><surname>Tsuchiya</surname><given-names>Fumito</given-names></name><xref ref-type="aff" rid="af3-dhps-4-019">3</xref></contrib></contrib-group><aff id="af1-dhps-4-019"><label>1</label>Graduate School of Engineering and Science, Shibaura Institute of Technology, Toyosu 3-7-5, Koto-ku, Tokyo, 135-8548 Japan</aff><aff id="af2-dhps-4-019"><label>2</label>Faculty of Engineering, Shibaura Institute of Technology, Toyosu 3-7-5, Koto-ku, Tokyo, 135-8548 Japan</aff><aff id="af3-dhps-4-019"><label>3</label>School of Pharmacy, International University of Health and Welfare, Minami-Aoyama 1-24-1, Minato-ku, Tokyo, 107-0062 Japan</aff><author-notes><corresp id="c1-dhps-4-019">Correspondence: Keita Nabeta, Toyosu 3-7-5, Koto-ku, Tokyo, 135-8548 Japan, Tel +81 3 5859 8507, Fax +81 3 5859 8507, Email <email>m709102@shibaura-it.ac.jp</email></corresp></author-notes><pub-date pub-type="collection"><year>2012</year></pub-date><pub-date pub-type="epub"><day>24</day><month>2</month><year>2012</year></pub-date><volume>4</volume><fpage>19</fpage><lpage>25</lpage><permissions><copyright-statement>© 2012 Nabeta et al, publisher and licensee Dove Medical Press Ltd.</copyright-statement><copyright-year>2012</copyright-year><license><license-p>This is an Open Access article which permits unrestricted noncommercial use, provided the original work is properly cited.</license-p></license></permissions><abstract><sec><title>Background</title><p>To prevent medical accidents, users must be informed of the cautions written in medical package inserts. To realize countermeasures by utilizing information systems, we must also implement a drug information database. However, this is not easy to develop, since the descriptions in package inserts are too complex and their information poorly structured. It is necessary to analyze package insert information and propose a data structure.</p></sec><sec><title>Methods</title><p>We analyzed the descriptions of ‘precautions for application’ in package inserts via text mining methods. In order to summarize statements, we applied dependency analysis to statements and visualized their relations between predicate words and other words. Furthermore, we extracted words representing timing to execute the order.</p></sec><sec><title>Results</title><p>We found that there are four types of statements: direct orders such as “
<inline-graphic xlink:href="dhps-4-019f6.jpg"></inline-graphic>” (use), causative orders such as “
<inline-graphic xlink:href="dhps-4-019f7.jpg"></inline-graphic>” (make someone use), direct interdictions such as “
<inline-graphic xlink:href="dhps-4-019f8.jpg"></inline-graphic>” (do not use), and causative interdictions such as “
<inline-graphic xlink:href="dhps-4-019f9.jpg"></inline-graphic>” (do not make user use). As for words representing timing, we extracted six groups: ”at the time of delivery,” “at the time of preparation,” “in use,” “after use,” and “at the time of storage.” From these results, we obtained points of consideration concerning the subjects of orders in the statements and timing of their execution.</p></sec><sec><title>Conclusion</title><p>From the obtained knowledge, we can define the information structure used to describe the precautionary statement. 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.</p></sec></abstract><kwd-group><kwd>medical safety</kwd><kwd>drug information</kwd><kwd>package insert</kwd><kwd>drug database</kwd><kwd>text mining</kwd></kwd-group></article-meta></front><body><sec><title>Introduction</title><p>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.</p><p>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)<xref ref-type="bibr" rid="b1-dhps-4-019">1</xref> has provided Standard General Markup Language (SGML) formatted data of package insert information since the year 1999.<xref ref-type="bibr" rid="b2-dhps-4-019">2</xref> Some medical institutions have developed drug databases based on the SGML data to retrieve drug information.<xref ref-type="bibr" rid="b3-dhps-4-019">3</xref> However, this is not easy since the descriptions in package inserts are too complex and their information poorly structured.<xref ref-type="bibr" rid="b4-dhps-4-019">4</xref>–<xref ref-type="bibr" rid="b8-dhps-4-019">8</xref> In this SGML data, most information is directly stored as statements and terms have not been standardized or encoded. Since computer systems cannot interpret drug information, existing systems cannot 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.</p><p>In this paper, we report on the results of analysis concerning the descriptions of ‘precautions for application’ in package inserts in terms of meaning via text mining methods. 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 also propose their data structure to organize the information in the descriptions.</p></sec><sec><title>Target data</title><p>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).<xref ref-type="bibr" rid="b9-dhps-4-019">9</xref> There were 11,685 SGML files.</p></sec><sec><title>Preprocessing</title><p>In our target SGML data, since precautionary statements are described in the “precautionsforapplication” field (as shown in <xref ref-type="fig" rid="f1-dhps-4-019">Figure 1</xref>), 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, we obtained 43,639 statements from 9,130 SGML data.</p><p>Additionally, we applied dependency analysis to the extracted statements via software named CaboCha (v0.53; Nara Institute of Science and Technology, Nara, Japan).<xref ref-type="bibr" rid="b10-dhps-4-019">10</xref> 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.</p></sec><sec><title>Analysis</title><sec><title>Extraction of predicate words</title><p>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. <xref ref-type="table" rid="t1-dhps-4-019">Table 1</xref> shows the top ten extracted words and their frequencies.</p><p>This result shows that “
<inline-graphic xlink:href="dhps-4-019f10.jpg"></inline-graphic>” (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, “
<inline-graphic xlink:href="dhps-4-019f11.jpg"></inline-graphic>” (must use). We therefore focused on verbs that modify “
<inline-graphic xlink:href="dhps-4-019f10.jpg"></inline-graphic>” (must), although there are other order expressions that are weaker, eg, “
<inline-graphic xlink:href="dhps-4-019f12.jpg"></inline-graphic>” (desirable) and that of possibility “
<inline-graphic xlink:href="dhps-4-019f13.jpg"></inline-graphic>” (can).</p></sec><sec><title>Extraction of order statements</title><p>To extract information concerning the order description, we collected verbs modifying “
<inline-graphic xlink:href="dhps-4-019f10.jpg"></inline-graphic>” (must) in the previous subsection and obtained 415 kinds of headwords in <xref ref-type="table" rid="t2-dhps-4-019">Table 2</xref>. In this table, we can see that there are verbs expressing direct orders such as “
<inline-graphic xlink:href="dhps-4-019f6.jpg"></inline-graphic>” (use), causative orders such as “
<inline-graphic xlink:href="dhps-4-019f7.jpg"></inline-graphic>” (make someone use), direct interdictions such as “
<inline-graphic xlink:href="dhps-4-019f8.jpg"></inline-graphic>” (do not use), and causative interdictions such as “
<inline-graphic xlink:href="dhps-4-019f9.jpg"></inline-graphic>” (do not make user use).</p><p>Since the descriptions of order and interdiction coexist in precautionary statements, medical staff can wrongly receive such information. 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.</p><p>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.<xref ref-type="bibr" rid="b11-dhps-4-019">11</xref> However, we cannot obtain relations between verbs and subjects/objects using this technique because the method extracts only nouns, verbs, and adjectives.</p><p>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, “
<inline-graphic xlink:href="dhps-4-019f14.jpg"></inline-graphic>” (wo), “
<inline-graphic xlink:href="dhps-4-019f15.jpg"></inline-graphic>” (ni), “
<inline-graphic xlink:href="dhps-4-019f16.jpg"></inline-graphic>” (ga), “
<inline-graphic xlink:href="dhps-4-019f17.jpg"></inline-graphic>” (kara), “
<inline-graphic xlink:href="dhps-4-019f18.jpg"></inline-graphic>” (de), “
<inline-graphic xlink:href="dhps-4-019f19.jpg"></inline-graphic>” (to), “
<inline-graphic xlink:href="dhps-4-019f20.jpg"></inline-graphic>” (e), “
<inline-graphic xlink:href="dhps-4-019f21.jpg"></inline-graphic>” (no), “
<inline-graphic xlink:href="dhps-4-019f22.jpg"></inline-graphic>” (yori), and “
<inline-graphic xlink:href="dhps-4-019f23.jpg"></inline-graphic>” (nite).</p><p>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 <xref ref-type="fig" rid="f2-dhps-4-019">Figures 2</xref>–<xref ref-type="fig" rid="f5-dhps-4-019">5</xref>.</p><sec><title>Direct order statements</title><p><xref ref-type="fig" rid="f2-dhps-4-019">Figure 2</xref> shows that major direct order verbs are “
<inline-graphic xlink:href="dhps-4-019f6.jpg"></inline-graphic>” (use), “
<inline-graphic xlink:href="dhps-4-019f24.jpg"></inline-graphic>” (be careful), and “
<inline-graphic xlink:href="dhps-4-019f25.jpg"></inline-graphic>” (stop); we can also see statements such as “
<inline-graphic xlink:href="dhps-4-019f26.jpg"></inline-graphic>” (use for eye drops), “
<inline-graphic xlink:href="dhps-4-019f27.jpg"></inline-graphic>” (inject into vein), and “
<inline-graphic xlink:href="dhps-4-019f28.jpg"></inline-graphic>” (use after washing).</p><p>It should be noted that the statement “
<inline-graphic xlink:href="dhps-4-019f29.jpg"></inline-graphic>” (avoid part of nerves) appeared in this network. Though it is an order to avoid, it can be interpreted as an interdiction, since the avoidance is implied in the concept of interdiction.</p></sec><sec><title>Direct interdiction statements</title><p><xref ref-type="fig" rid="f3-dhps-4-019">Figure 3</xref> 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 “
<inline-graphic xlink:href="dhps-4-019f30.jpg"></inline-graphic>” (do not apply to inflammatory part). Moreover, we obtained statements that indicate an interdiction of mixing drugs, eg, “
<inline-graphic xlink:href="dhps-4-019f31.jpg"></inline-graphic>” (do not mix with drugs).</p></sec><sec><title>Causative order statements</title><p>In <xref ref-type="fig" rid="f4-dhps-4-019">Figure 4</xref>, we see two types of causative verbs. The first is for medical experts and patients, and another type is for medicines.</p><p>The former gives examples such as “
<inline-graphic xlink:href="dhps-4-019f32.jpg"></inline-graphic>” (make patients take the medicine without chewing), “
<inline-graphic xlink:href="dhps-4-019f33.jpg"></inline-graphic>” (make patients take the drug after a meal), and “
<inline-graphic xlink:href="dhps-4-019f34.jpg"></inline-graphic>” (make patients take the medicine with water). These are statements that indicate orders to patients via pharmacists and nurses.</p><p>The latter gives examples such as “
<inline-graphic xlink:href="dhps-4-019f35.jpg"></inline-graphic>” (neutralize) and “
<inline-graphic xlink:href="dhps-4-019f36.jpg"></inline-graphic>” (dry). They express methods for the preparation and storage of medicines.</p></sec><sec><title>Causative interdiction statements</title><p>As causative order statements already mentioned, the subjects of the former include both humans (pharmacists, nurses and patients) and medicines. In <xref ref-type="fig" rid="f5-dhps-4-019">Figure 5</xref>, statements such as “
<inline-graphic xlink:href="dhps-4-019f37.jpg"></inline-graphic>” (do not make patients avoid water) and “
<inline-graphic xlink:href="dhps-4-019f38.jpg"></inline-graphic>” (do not regurgitate from syringe) can be found.</p><p>Based on these results, we found two points concerning precautionary statements that should be considered. The first is that both order statements and interdiction statements chaotically coexist. 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.</p></sec></sec></sec><sec><title>Extraction of words representing timing</title><p>In the previous section, we found words that express the time to perform an action eg, “
<inline-graphic xlink:href="dhps-4-019f39.jpg"></inline-graphic>” (before administration) and “
<inline-graphic xlink:href="dhps-4-019f40.jpg"></inline-graphic>” (in use). 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.</p><p>Initially, we compiled keywords where the part of speech is an adverbial noun (<xref ref-type="table" rid="t3-dhps-4-019">Table 3</xref>). We defined “
<inline-graphic xlink:href="dhps-4-019f41.jpg"></inline-graphic>” (case), “
<inline-graphic xlink:href="dhps-4-019f42.jpg"></inline-graphic>” (before), “
<inline-graphic xlink:href="dhps-4-019f43.jpg"></inline-graphic>” (after), “
<inline-graphic xlink:href="dhps-4-019f44.jpg"></inline-graphic>”/“
<inline-graphic xlink:href="dhps-4-019f45.jpg"></inline-graphic>” (time), and “
<inline-graphic xlink:href="dhps-4-019f46.jpg"></inline-graphic>” (when) as feature words that express the concept of timing, since most segments expressing timings contain these words.</p><p>Next, we extracted expressions of timing via the characters defined above. 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, “
<inline-graphic xlink:href="dhps-4-019f47.jpg"></inline-graphic>” (be careful at the time of use), and another of which where the timing expression appears singly eg, “
<inline-graphic xlink:href="dhps-4-019f48.jpg"></inline-graphic>” (delivery time of medicines) in the “- ” 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. We regarded the keywords obtained as timing to execute the order.</p>
<p>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,” “after use,” and “at the time of storage.” We show the results of this classification in <xref ref-type="table" rid="t4-dhps-4-019">Table 4</xref>.</p><p>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, “
<inline-graphic xlink:href="dhps-4-019f49.jpg"></inline-graphic>” (dispose of) after use and “
<inline-graphic xlink:href="dhps-4-019f50.jpg"></inline-graphic>” (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.</p></sec><sec><title>Conclusion</title><p>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.</p><p>Utilizing the knowledge obtained, we can define the information structure used to describe the precautionary statement. 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.</p><p>In future work, we will analyze other information in the package inserts and develop a drug information database based on the results.</p></sec></body><back><fn-group><fn id="fn2-dhps-4-019"><p><bold>Disclosure</bold></p><p>The authors declare no conflicts of interest in this work.</p></fn></fn-group><ref-list><title>References</title><ref id="b1-dhps-4-019"><label>1</label><element-citation publication-type="webpage"><source>Menu of package insert information: Pharmaceuticals and medical devices agency</source><comment>[cited October 15, 2007] Available from: <ext-link ext-link-type="uri" xlink:href="http://www.pmda.go.jp/">http://www.pmda.go.jp/</ext-link></comment><date-in-citation>Accessed September 6, 2011</date-in-citation></element-citation></ref><ref id="b2-dhps-4-019"><label>2</label><element-citation publication-type="journal"><person-group 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Available from: <ext-link ext-link-type="uri" xlink:href="http://code.google.com/p/cabocha/">http://code.google.com/p/cabocha/</ext-link></comment><date-in-citation>Accessed September 30, 2011</date-in-citation></element-citation></ref><ref id="b11-dhps-4-019"><label>11</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kimura</surname><given-names>M</given-names></name><name><surname>Furukawa</surname><given-names>H</given-names></name><name><surname>Tsukamoto</surname><given-names>H</given-names></name><etal></etal></person-group><article-title>The analysis of questionnaires about safety of drug use, the application of text mining to free description questionnaires</article-title><source>Japanese Journal of Ergonomics</source><year>2005</year><volume>41</volume><issue>5</issue><fpage>297</fpage><lpage>305</lpage></element-citation></ref></ref-list></back><floats-group><fig id="f1-dhps-4-019" position="float"><label>Figure 1</label><caption><p>Sample data of precautionary statement.</p></caption><graphic xlink:href="dhps-4-019f1"></graphic></fig><fig id="f2-dhps-4-019" position="float"><label>Figure 2</label><caption><p>Network of words related to direct orders (threshold: 90).</p></caption><graphic xlink:href="dhps-4-019f2"></graphic></fig><fig id="f3-dhps-4-019" position="float"><label>Figure 3</label><caption><p>Network of words related to direct interdiction (threshold: 50).</p></caption><graphic xlink:href="dhps-4-019f3"></graphic></fig><fig id="f4-dhps-4-019" position="float"><label>Figure 4</label><caption><p>Network of words related to indirect orders (threshold: 5).</p></caption><graphic xlink:href="dhps-4-019f4"></graphic></fig><fig id="f5-dhps-4-019" position="float"><label>Figure 5</label><caption><p>Network of words related to indirect interdiction (threshold: 0).</p></caption><graphic xlink:href="dhps-4-019f5"></graphic></fig><table-wrap id="t1-dhps-4-019" position="float"><label>Table 1</label><caption><p>Extracted predicate words (top 10)</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" rowspan="1" colspan="1">Predicate words</th><th align="left" valign="top" rowspan="1" colspan="1">Frequency</th><th align="left" valign="top" rowspan="1" colspan="1">Ratio (%)</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f10.jpg"></inline-graphic> (must)</td><td align="left" valign="top" rowspan="1" colspan="1">24,929</td><td align="left" valign="top" rowspan="1" colspan="1">82.86</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f12.jpg"></inline-graphic> (desirable)</td><td align="left" valign="top" rowspan="1" colspan="1">1578</td><td align="left" valign="top" rowspan="1" colspan="1">5.24</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f51.jpg"></inline-graphic> (exist)</td><td align="left" valign="top" rowspan="1" colspan="1">812</td><td align="left" valign="top" rowspan="1" colspan="1">2.70</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f52.jpg"></inline-graphic> (must wash)</td><td align="left" valign="top" rowspan="1" colspan="1">250</td><td align="left" valign="top" rowspan="1" colspan="1">0.83</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f53.jpg"></inline-graphic> (caution)</td><td align="left" valign="top" rowspan="1" colspan="1">194</td><td align="left" valign="top" rowspan="1" colspan="1">0.64</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f41.jpg"></inline-graphic> (case)</td><td align="left" valign="top" rowspan="1" colspan="1">105</td><td align="left" valign="top" rowspan="1" colspan="1">0.35</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f13.jpg"></inline-graphic> (can)</td><td align="left" valign="top" rowspan="1" colspan="1">73</td><td align="left" valign="top" rowspan="1" colspan="1">0.24</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f54.jpg"></inline-graphic> (mucous membrane)</td><td align="left" valign="top" rowspan="1" colspan="1">68</td><td align="left" valign="top" rowspan="1" colspan="1">0.23</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f55.jpg"></inline-graphic> (do)</td><td align="left" valign="top" rowspan="1" colspan="1">51</td><td align="left" valign="top" rowspan="1" colspan="1">0.17</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f56.jpg"></inline-graphic> (keep the eyelids apart)</td><td align="left" valign="top" rowspan="1" colspan="1">47</td><td align="left" valign="top" rowspan="1" colspan="1">0.16</td></tr></tbody></table></table-wrap><table-wrap id="t2-dhps-4-019" position="float"><label>Table 2</label><caption><p>Extracted order words (top 10)</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" rowspan="1" colspan="1">Order words</th><th align="left" valign="top" rowspan="1" colspan="1">Frequency</th><th align="left" valign="top" rowspan="1" colspan="1">Ratio (%)</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f57.jpg"></inline-graphic> (guide)</td><td align="left" valign="top" rowspan="1" colspan="1">4771</td><td align="left" valign="top" rowspan="1" colspan="1">18.27</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f24.jpg"></inline-graphic> (be careful)</td><td align="left" valign="top" rowspan="1" colspan="1">3696</td><td align="left" valign="top" rowspan="1" colspan="1">14.15</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f6.jpg"></inline-graphic> (use)</td><td align="left" valign="top" rowspan="1" colspan="1">3417</td><td align="left" valign="top" rowspan="1" colspan="1">13.08</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f8.jpg"></inline-graphic> (do not use)</td><td align="left" valign="top" rowspan="1" colspan="1">2440</td><td align="left" valign="top" rowspan="1" colspan="1">9.34</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f58.jpg"></inline-graphic> (inject)</td><td align="left" valign="top" rowspan="1" colspan="1">986</td><td align="left" valign="top" rowspan="1" colspan="1">3.78</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f59.jpg"></inline-graphic> (avoid)</td><td align="left" valign="top" rowspan="1" colspan="1">968</td><td align="left" valign="top" rowspan="1" colspan="1">3.71</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f60.jpg"></inline-graphic> (administer)</td><td align="left" valign="top" rowspan="1" colspan="1">918</td><td align="left" valign="top" rowspan="1" colspan="1">3.52</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f61.jpg"></inline-graphic> (perform)</td><td align="left" valign="top" rowspan="1" colspan="1">760</td><td align="left" valign="top" rowspan="1" colspan="1">2.91</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f62.jpg"></inline-graphic> (do not perform)</td><td align="left" valign="top" rowspan="1" colspan="1">436</td><td align="left" valign="top" rowspan="1" colspan="1">1.67</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f55.jpg"></inline-graphic> (do)</td><td align="left" valign="top" rowspan="1" colspan="1">420</td><td align="left" valign="top" rowspan="1" colspan="1">1.61</td></tr></tbody></table></table-wrap><table-wrap id="t3-dhps-4-019" position="float"><label>Table 3</label><caption><p>Extracted adverbial nouns (top 10)</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" rowspan="1" colspan="1">Adverbial nouns</th><th align="left" valign="top" rowspan="1" colspan="1">Frequency</th><th align="left" valign="top" rowspan="1" colspan="1">Ratio (%)</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f41.jpg"></inline-graphic> (case)</td><td align="left" valign="top" rowspan="1" colspan="1">2336</td><td align="left" valign="top" rowspan="1" colspan="1">25.13</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f63.jpg"></inline-graphic> (time of stinging)</td><td align="left" valign="top" rowspan="1" colspan="1">599</td><td align="left" valign="top" rowspan="1" colspan="1">6.44</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f64.jpg"></inline-graphic> (because)</td><td align="left" valign="top" rowspan="1" colspan="1">536</td><td align="left" valign="top" rowspan="1" colspan="1">5.77</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f43.jpg"></inline-graphic> (after)</td><td align="left" valign="top" rowspan="1" colspan="1">357</td><td align="left" valign="top" rowspan="1" colspan="1">3.84</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f65.jpg"></inline-graphic> (after opening)</td><td align="left" valign="top" rowspan="1" colspan="1">346</td><td align="left" valign="top" rowspan="1" colspan="1">3.72</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f45.jpg"></inline-graphic> (time)</td><td align="left" valign="top" rowspan="1" colspan="1">303</td><td align="left" valign="top" rowspan="1" colspan="1">3.26</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f66.jpg"></inline-graphic> (long-term)</td><td align="left" valign="top" rowspan="1" colspan="1">235</td><td align="left" valign="top" rowspan="1" colspan="1">2.53</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f67.jpg"></inline-graphic> (extended period)</td><td align="left" valign="top" rowspan="1" colspan="1">218</td><td align="left" valign="top" rowspan="1" colspan="1">2.34</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f68.jpg"></inline-graphic> (time of ampoule cutting)</td><td align="left" valign="top" rowspan="1" colspan="1">160</td><td align="left" valign="top" rowspan="1" colspan="1">1.72</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f69.jpg"></inline-graphic> (after dissolving)</td><td align="left" valign="top" rowspan="1" colspan="1">153</td><td align="left" valign="top" rowspan="1" colspan="1">1.65</td></tr></tbody></table></table-wrap><table-wrap id="t4-dhps-4-019" position="float"><label>Table 4</label><caption><p>Classification of words meaning time</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" rowspan="1" colspan="1">Class</th><th align="left" valign="top" rowspan="1" colspan="1">Example of extracted words</th><th align="left" valign="top" rowspan="1" colspan="1">Frequency</th><th align="left" valign="top" rowspan="1" colspan="1">Ratio (%)</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">Time of delivery</td><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f48.jpg"></inline-graphic> (delivery time of medicines)</td><td align="left" valign="top" rowspan="1" colspan="1">3791</td><td align="left" valign="top" rowspan="1" colspan="1">13.77</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Time of preparation</td><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f70.jpg"></inline-graphic> (time of preparation),
<inline-graphic xlink:href="dhps-4-019f71.jpg"></inline-graphic> (time of cutting ampoule), etc</td><td align="left" valign="top" rowspan="1" colspan="1">6067</td><td align="left" valign="top" rowspan="1" colspan="1">22.04</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">In use</td><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f72.jpg"></inline-graphic> (in administration),
<inline-graphic xlink:href="dhps-4-019f73.jpg"></inline-graphic> (time of injection), etc</td><td align="left" valign="top" rowspan="1" colspan="1">12,599</td><td align="left" valign="top" rowspan="1" colspan="1">45.77</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">After use</td><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f74.jpg"></inline-graphic> (after administration),
<inline-graphic xlink:href="dhps-4-019f75.jpg"></inline-graphic> (after use)</td><td align="left" valign="top" rowspan="1" colspan="1">279</td><td align="left" valign="top" rowspan="1" colspan="1">1.01</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Time of storage</td><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f76.jpg"></inline-graphic> (time of storage)</td><td align="left" valign="top" rowspan="1" colspan="1">406</td><td align="left" valign="top" rowspan="1" colspan="1">1.48</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Others</td><td align="left" valign="top" rowspan="1" colspan="1"><inline-graphic xlink:href="dhps-4-019f77.jpg"></inline-graphic> (in the inevitable case),
<inline-graphic xlink:href="dhps-4-019f78.jpg"></inline-graphic> (for the repetitive use), etc</td><td align="left" valign="top" rowspan="1" colspan="1">1438</td><td align="left" valign="top" rowspan="1" colspan="1">5.23</td></tr></tbody></table></table-wrap></floats-group></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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