Finding Every Medical Termby Life Science Dictionary for MedNLP	
Shuji Kaneko1, Nobuyuki Fujita2, Hiroshi Ohtake3	
1Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
2National Institute of Technology and Evaluation, Tokyo, Japan
3Center for Arts and Sciences, Fukui Prefectural University, Fukui, Japan	

ABOUT US	
• The Life Science Dictionary (LSD) project, founded in 1993, is a research project todevelop a systematic database for life science (of course, including medical) termsand tools for the convenience of life scientists. 	

• Our services are designed to provide and encourage access within the scientificcommunity to the most up-to-date and comprehensive information on EnglishJapanese translation dictionary of life science terms. 	

• In keeping with the users' expectations, we have been enriching and refining the 
database records to a medical thesaurus 
compatible with MeSH (Medical Subject Headings developed by National Library 
of Medicine, USA) thesaurus.	

• Recent 2013 version of LSD contains approximately 30 thousand headings with 200 thousand English and Japanese synonyms, consisting of the names of 
anatomical concepts, biological organisms,
chemical compounds, methods, disease and symptoms. 	

AIM OF THIS STUDY	• One of the practical applications of thesaurus is text mining. 	

• For example, adverse drug events can be rapidly extracted by finding the causal
relationship of drug treatment and related symptoms recorded in medical records.
Ex. Allergy + An antihistamine → arrhythmia = a side effect of the antihystamine	• Favorably, we have previously developed a series of gloss-embedding Perl scripts
for medical English texts. 	

• In this study, therefore 
we aimed to find every 
medical term (English or Japanese) as many as possible.
• As the source text, an NTCIR10 test set 
(1,121 sentences) was used.	

METHOD – DICTIONARY	• A tagger dictionary was made from LSD database as an EUC text file, whichcontains approximately 200,000 rows and 4 columns: 	(1) synonym strings	(2) subject heading strings (converged to 30,000 descroptors)
	(3) category of term	(4) subject heading ID (from MeSH) for external reference link	• For the category of terms, all terms were classified and marked by one of thefollowing categories according to the MeSH tree: anatomy, biological, disease, molecule, method, and knowledge.		

METHOD - PERL SCRIPT	• To take full advantage of the LSD in which many phrases have beenregistered, “the longest matches first” principle was adopted in thematching process. 	

• For this purpose, the tagger dictionary was sorted in the descending order ofbyte lengths, and text matching was performed for each of the dictionary
entries in this order.	

• Both the text and the dictionary were first converted to EUC, and they were treated as byte strings in the matching process. 	

• All two-byte roman characters were converted to corresponding ASCII characters.	

• For better readability of result, a standard HTML format was used, in which “class” attribute was assigned to each of the category. 	

THE OUTPUT	
• From the 0.1 MB test
document, 2,569 terms(including English spellings)
were tagged and isolatedin 2 min by personal PC. 	

EASY REFERENCE	• We also added a ‘mouse-over heading’feature, in which the embedded subject
heading of the term will be displayedwhen the cursor was placed over thetagged term. 	

• In addition, by clicking the tagged part,the user can confirm the thesaurus entry
in our WebLSD online 	• This allows users to judge the justnessof tagged terms.	
click	MISSED TERMS	• In addition to many correctly-tagged terms, several patterns of missed orincorrect tags were found. 	

• The mostly missed terms were English abbreviations. 	

• Especially, in the description of clinical test data, a variety of abbreviations andacronyms were used, which cannot be marked. 	

• Since the meanings of 2- or 3-word abbreviations are ambiguous, we hadomitted most of the abbreviations from tagger dictionary. 	

• However, if we know the part of document is apparently indicating clinicaldata, we can make a specific tagger dictionary for clinical tests. 	

WRONG-TAGGED TERMS	• The most typical pattern of incorrect tag was ‘partly-tagged’ term. 	

• In these cases, part of unit concepts were registered in the dictionary.	

• However, the combination of two or more concepts is common particularlyin the names of disease and symptom, which were not completely coveredin our thesaurus.	

• In these cases, we have to expand our dictionary.	

MISSPELLING AND TYPO	
• To our surprise, there were many misspellings and typographical errors,even in Japanese terms, in the test document. 	

• Precise text matching did not tag incorrect spellings that medical doctor canrecognize their meanings. 	

SUMMARY	• With our tagging dictionary and Perl scripts, most of medical terms wereeasily marked and visualized as an HTML document. 	

• From the 0.1 MB test document, 2,569 terms (including English spellings)
were tagged and visualized in a color HTML format. 	

• Additional ‘mouse-over heading’ and web reference enables easy
reviewing of the tagged terms. 	

• Through this task, we have learnt the potential and limitation of ourthesaurus and scripts in finding medical terms from given Japanese texts. 	

• This process has a limitation in assigning ambiguous abbreviations andmisspelled words. 	

• Moreover, there is an insurmountable difficulty to accomplish a ‘perfectmatching’ with a fixed text dictionary, since improvement of thesaurus isa laborious work. 	

• The simple tagging strategy might be useful in preprocessing of medical
reports.	

• Combination of natural text processing with this tool will be convenient forthe practical use.