臺灣博碩士論文加值系統

藥品治療除了療效和藥品副作用外，藥品交互作用是特別需要注意的。每年都有許多藥品上市，而病患發生新藥和其他藥品之間有交互作用的事件，可能被報告於醫學文獻中。所以基於病患的用藥安全，事先發現藥品交互作用是很重要的事情，而在此我們試圖找出個方法來發掘可能隱藏的藥品交互作用。
本研究使用文字探勘的技術，利用由Swanson和Smalhesier發展出的軟體ARROWSMITH來作為我們研究的工具。ARROWSMITH利用連結兩個不同主題的方式便利於發現值得研究的假說。藉著ARROWSMITH由資料庫中建造出引發聯想的並列列表，幫助科學的發現。本研究先以digoxin、repaglinide來測試ARROWSMITH是否符合本研究的需求，回溯性的鑑定藥品間的交互作用。接著進行尋找新核准上市的藥品可能隱藏的交互作用。包括rosuvastatin、duloxetine、eplerenone、ezetimibe和 memantine。
在新藥的研究結果，推測rosuvastatin、duloxetine可能有隱藏的交互作用，但仍需要進一步的研究證實。至於在eplerenone、ezetimibe和memantine中以本研究方法在此並未發現可能隱藏的藥品交互作用。

Drug interactions are worth to emphasize in safety use of drugs. A number of drugs are introduced into the market every year, and patients who have been identified drug-drug interactions between the new approved drug and other medications would be reported in medical literature. Based on safety concern of patients, to discovery drug interactions beforehand is very important. So we attempted to find a way to uncover some possible and hidden drug-drug interactions of a new drug.
We used a text mining tool created by Swanson and Smalheiser called ARROWSMITH that facilitates the discovery of plausible hypotheses linking findings across specialties. By creating suggestive juxtapositions of searched and filtered records, ARROWSMITH is a useful tool for scientific discovery but not in itself a mechanism of scientific discovery. Digoxin and repaglinide were used as the model drugs to validate if ARROWSMITH could satisfy what we need in the area of identifying drug-drug interactions. Then we studied drug-drug interactions of some newly approved drugs, which included rosuvastatin, duloxetine, eplerenone, ezetimibe and memantine.
In summary, we have found some hidden but possibly potential drug-drug interactions with rosuvastatin or duloxetine and they must further evaluation to comfirm. Based on results of eplerenone, ezetimibe and memantine, we could not uncover some possible and hidden drug interactions for those drugs.

目錄 -------------------------------------------------------------- I
圖目錄 ------------------------------------------------------------ IV
表目錄 ------------------------------------------------------------ V
中文摘要 ---------------------------------------------------------- VI
英文摘要 ---------------------------------------------------------- VII
第一章 緒論-------------------------------------------------------- 1
第一節 研究背景 --------------------------------------------------- 1
一、 藥品交互作用 ------------------------------------------------- 1
二、 資料來源 ----------------------------------------------------- 4
第二節 研究目的 --------------------------------------------------- 6
第二章 文獻討論 ---------------------------------------------------10
第一節 文字探勘綜述------------------------------------------------16
一、 資料、資訊及知識 ---------------------------------------------16
二、 資料庫 -------------------------------------------------------17
三、 資訊檢索及資訊萃取 -------------------------------------------18
四、 資料探勘 -----------------------------------------------------19
五、 資料探勘與知識發掘 -------------------------------------------20
六、 PubMed資料庫 -------------------------------------------------25
七、 文字探勘 -----------------------------------------------------28
第二節 文字探勘的介紹 ---------------------------------------------29
一、 文字探勘的定義 -----------------------------------------------29
二、 文字探勘的應用 -----------------------------------------------29
三、 文字探勘的方法 ------------------------------------------30
四、 文字探勘的架構 -----------------------------------------------32
五、 以概念為基礎的應用 -------------------------------------------33
六、 文字探勘、資訊檢索及資料探勘 ---------------------------------34
第三節 假說的發掘 -------------------------------------------------35
一、 歷史演進 ------------------------------------------------35
二、 開放式發掘 ----------------------------------------------38
三、 閉鎖式發掘 ----------------------------------------------39
四、 ARROWSMITH ---------------------------------------------40
第四節 研究藥品個論 -----------------------------------------------46
一、 Digoxin--------------------------------------------------46
二、 Verapamil------------------------------------------------46
三、 Repaglinide (Prandin®)-----------------------------------47
四、 Gemfibrozil (Lopid®)-------------------------------------47
五、 Rosuvastatin (Crestor®) ---------------------------------48
六、 Duloxetine (Cymbalta®) ----------------------------------51
七、 Eplerenone (Inspra®) ------------------------------------53
八、 Ezetimibe (Zetia®) --------------------------------------55
九、 Memantine (Namenda®) ------------------------------------56
第三章 研究方法 ---------------------------------------------------58
第一節 測試ARROWSMITH是否適用 -------------------------------------58
第二節 新藥研究 ---------------------------------------------------60
第四章 研究結果 ---------------------------------------------------62
第五章 研究討論 ---------------------------------------------------70
第六章 結論及未來展望 ----------------------------------------78
第七章 參考文獻 --------------------------------------------79
第八章 圖表與說明 -------------------------------------------88

何光國，圖書資訊組織原理。台北：三民，民79
藥品安全簡訊Drug Safety Newsletter, (2004), 6, 3
http://adr.doh.gov.tw/manage/Data/B01/35.pdf
Ahonen H., Heinonen O., Klemettinen M. & Verkamo A. I. (1998),
“Applying data mining techniques for descriptive phrase extraction in
digital document collections”, In Proceedings of the IEEE Forum on
Research and Technology Advances in Digital Libraries, pp. 2-11
Saanta Barbara, CA
Ament W., Bertolino G. & Liszewski L. (2000), “Clinically Significant
Drug Interactions”, American Family Physician, 61: 1745-54.
Andrade A. & Valencia A. (1998), “Automatic extraction of keywords
from scientific text: application to the knowledge domain of protein
families”, Bioinformatics, 14(7): 600-607.
AstraZeneca Pharmaceuticals. (2005), http://www.crestor.com
Blagosklonny M. V. & Pardee A. B. (2002), “Unearthing the Gems”,
Nature, 416, 373.
Baeza-Yates R. & Ribeiro- Neto B. (1999), “Modern Information
Retrieval”, Addison-Wesley Longman Publishing Company
Birmes P., Coppin C., Schmitt L. & Lauque D. (2003), “Serotonin
syndrome: a brief review”, Canadian Medical Association Journal, 168:
11, 1439-1442.
Boyer E. W. & Shannon M. (2005), “The Serotonin Syndrome”, New
England journal of medicine, 352: 11, 1112-1120.
Cooper K. J., Martin P. D., Dane A. L., Warwick M. J., Raza A. &
Schneck D. W. (2003), “The Effect of Erythromycin on the
80
Pharmacokinetics of Rosuvastatin”, Europe Journal of Clinical
Pharmacology, 59(1): 51-56.
DiGiacomo R. A., Kremer J. M. & Shah D. M. (1989), “Fish Oil Dietary
Supplementation in Patients with Raynaud’s phenomenon: a
Double-blind, Controlled, Prospective Study”, American Journal of
Medicine, 8: 158-164.
Dorre J., Gerstl P. & Seiffert R. (1999), “Text Mining: Finding Nuggets
in Mountains of Textual Data”, In Proceedings of the Fifth International
Conference on Knowledge Discovery and Data Mining (KDD-99):
398-401. San Diego, CA
Eli Lilly Pharmaceuticals. (2004, 2005), http://www.cymbalta.com
Fayyad U. M., Piatetsky-Shapiro G. & Smyth P. (1996), “From Data
Mining to Knowledge Discovery: An Overview”, Advances in
Knowledge Discovery and Data Mining, AAAI Press / The MIT Press,
Menlo Park, CA, p1-34.
Fayyad U. M., Piatetsky-Shapiro G. & Smyth P. (1996), “From Data
Mining to Knowledge Discovery in Database”, AI magazine, 17(3):
37-54.
Fayyad U. M. & Paul S. (1997), “Data mining and KDD: Promise and
challenges”, Future Generation computer Systems, 13: 99-115.
Forest Pharmaceuticals. (2005), http://www.namenda.com
Food and Drug Administration, U.S. Department of Health and Human
Services (2000a), “FDA Updates Warnings for Cisapride”, FDA talk
paper, January 24.
Food and Drug Administration, U.S. Department of Health and Human
Services (2000b), “Janssen Pharmaceutica Stops Marketing
Cisapride in the US”, FDA talk paper, March 23.
Feldman R. & Dagan I. (1995), “Knowledge discovery in textual
databases”, In Proceedings of KDD, International Conference in
Knowledge Discovery and Data Mining, Montreal, Canada, 112-117.
81
Feldman R. & Hirsh H. (1997), “Exploiting Background Information in
Knowledge Discovery from Text”, Journal of Intelligent Information
Systems, 9(1): 83-97.
Gordon M.D. & Lindsay R.K. (1996), “Toward Discovery Support
Systems: A Replication, Reexamination and Extension of Swanson’s
work on Literature-based Discovery of a Connection between
Raynaud’s and Fish Oil”, Journal of the American Society for
Information Science, 47: 116-128.
Grishman R. (1997), “Information extraction: Techniques and
challenges”, Information Extraction: A Multidisciplinary Approach to an
Emergine Information Technology, Vol. 1299: 10-27.
Hahn U. & Schnattinger K. (1997), “Deep knowledge discovery from
natural language texts”, In: Proceedings of KDD, International
Conference in Knowledge Discovery and Data Mining,
Newport Beach, CA.: 175-178.
Han J. & Kamber M. (2000), “Data Mining: Concepts and Techniques”,
Morgan Kaufmann, San Francisco
Hand D. J. (1999), “Statistics and Data Mining: Interesting Disciplines”,
Association for Computers and Machinery SIGKDD Explorations, Vol.
1 (1): 16 - 19.
Hearst M. A. (1999), “Untangling Text Data Mining”, In Proceedings of
the 37th Annual Meeting of the Association for Computational
Linguistics (ACL''99), College Park, MD, USA.
Hristovski D., Stare J., Peterlin B. & Dzeroski S. (2001), “Supporting
discovery in medicine by association rule mining in Medline and
UMLS”, In Proceedings of MedInfo Conference,London, England,
10(2): 1344-1348.
Kantardzic M. (2003), “Data Mining: Concepts, Models, Methods, and
Algorithms”, IEEE Computer Society, Sponser
Karanikas H., Tjortjis C. & Theodoulidis B. (2000), “An approach to
82
Text Mining using Information Extraction”, PKDD 2000 Knowledge
Management: Theory and Applications.
Klein H. O., Lang R., Di Segni E. & Kaplinsky E. (1980),
“Verapamil-digoxin interaction”, N Engl J Med. 17; 303(3): 160.
Lent B., Agrawal R. & Srikant R. (1997), “Discovering Trends in Text
Databases”, In Proceedings of KDD, International Conference on
Knowledge Discovery, NewPort Beach, CA, 227-230.
Lindsay R. K. & Gordon M. D. (1999), “Literature-based Discovery by
Lexical Statistics”, Journal of the American Society for Information
Science, 50(7): 574-587.
Makoid M., Vuchetich P. & Banakar U. (1996), “Basic
Pharmacokinetics”,
http://pharmacy.creighton.edu/pha443/pdf/Default.asp
Masys D. R., Welsh J. B., Fink J. L., Gribskov M., Klacansky I. &
Corbeil, J. (2001), “Use of keyword hierarchies to interpret gene
expression patterns”, Bioinformatics, 17(4) 319-326.
Merck/ Schering- Plough Pharmaceuticals, (2001, 2002), http://
www.zetia.com/
MICROMEDEX® Helthcare Series, (2005), Copyright 2005. Thomson
MICROMEDEX.
Nada L. (1998), “Data mining in medicine: Selected techniques and
applications”, Second International Conference on the Practical
Application of Knowledge Discovery and Data Mining, London, 25-27
Mar.
Niemi M., Backman J. T., Neuvonen M. & Neuvonen P. J. (2003),
“Effects of gemfibrozil, itraconazole and their combination on the
pharmacokinetics and pharmacodynamics of repaglinide: potentially
hazardous interaction between gemfibrozil and repaglinide”,
Diabetologia, 46(3): 347-51. Epub 2003 Feb 27
83
NLM. PubMed, (2004).
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
NLM. Unified medical language system knowledge sources, (2004).
http://umlsks.nlm.nih.gov/
Novo Nordisk Pharmaceuticals, (2005), http://www.prandin.com
Pasternak R. C., Smith S.C., Bairey-Merz C. N., Grundy S.M.,
Cleeman J.I. &Lenfant C. ACC/AHA/NHLBI (2002), “Clinical advisory
on the use and safety of statins”, J. AM. Coll. Cardiol.40: 567-572.
Pfizer Pharmaceuticals. (2005), http://www.inspra.com
Piatetsky-Shapiro G. & Frawley W. J. E. (1991), “Knowledge
discovery in databases”, Cambridge, MA: MIT Press.
Quinlan J. R. (1993), “C4.5: Programs for Machine Learning”, Morgan
Kaufmann, San Mateo, CA
Rajman M. and Besancon R. (1997), “Text Mining: Natural Language
Techniques and Text Mining Applications”, Artificial Intelligence
Laboratory, Computer Science Department, Swiss Federal Institute of
Technology, IFIP.
Ross B. M., Hudson C., Erlich J., Warsh J. J. & Kish S. J. (1997), “
Increased phospholipid breakdown in schizophrenia: evidence for the
involvement of a calcium-independent phospholipase A2”, Arch. Gen.
Psychiatry 54: 487–494.
Sekimizu T., Park H. S. & Tsujii J. (1998), “Identifying the Interaction
between Genes and Gene Products Based on Frequently Seen Verbs
in Medline Abstracts”, Genome Informatics 9: 62-71.
Shatkay H., Edwards S., Wilbur W. J. & Boguski M. (2000), “Genes,
Themes and Microarrays. Using information retrieval for large-scale
gene analysis”, In Proceedings of Intelligent Systems for Molecular
Biology, La Jolla, California, 317-328.
84
Singh L., Scheuermann P & Bin Chen. (1997), “Generating
Association Rules from Semi-structured Documents using an
Extended Concept Hierarchy”, In Proceedings of the 6th International
Conference on Information and Knowledge Management. Las Vegas,
Nevada, USA, p193-200
Skyrme D. J. (1999), “Knowledge Networking- Creating the
Collaborative Enterprise”, Published by Butterworth- Heinemann
Company, Inc. Oxford, UK
Smalheiser N. R. & Swanson D. R. (1994), “Assessing a gap in the
biomedical literature: Magnesium deficiency and neurologic disease”,
Neuroscience Research Communications, 15: 1-9.
Smalheiser N. R. & Swanson D. R. (1996a), “Indomethacin and
Alzheimer''s Disease”, Neurology, 46:583.
Smalheiser N. R. & Swanson D. R. (1996b), “Linking estrogen to
Alzheimer''s disease: An informatics approach”, Neurology, 47:
809-810.
Smalheiser N. R. & Swanson D. R. (1998a), “Calcium-independent
phospholipase A2 and schizophrenia”, Archives of General Psychiatry,
55: 752-753.
Smalheiser N. R. & Swanson D. R. (1998b), “Using Arrowsmith: A
computer-assisted approach to forming and assessing scientific
hypotheses”, Computer Methods and Programs in Biomedicine, 57:
149-153.
Srinivasan P. (2001), ”MeSHmap: A text mining tool for MEDLINE”, In
Proceedings of AMIA, the Annual Conference of the American
Medical Informatics Association, Washington, D.C., 642-646.
Srinivasan P. (2004), “Text Mining: Generating Hypotheses from
MEDLINE”, Journal of the American Society for Information Science,
555(5): 396-413.
Srinivasan P., Libbus B. & Sehgal A. K. (2004), ”Mining MEDLINE:
Postulating a Beneficial Role for Curcumin Longa in Retinal Diseases”,
85
BioLINK 2004: Linking Biological Literature, Ontologies and
Databases, 33-40.
Srinivasan P. & Libbus B. (2004), ”Mining MEDLINE for Implicit Links
between Dietary Substances and Diseases”, Bioinformatics, 20(1):
i290-i296.
Swanson D. R. (1986a), “Fish oil, Raynaud''s syndrome, and
undiscovered public knowledge”, Perspectives in Biology and
Medicine, 30(l): 7-I5.
Swanson D. R. (I986b), “Undiscovered public knowledge”, Library
Quarterly, 56: 103-118.
Swanson D. R. (1987), “Two medical literatures that are logically but
not bibliographically connected”, Journal of the American Society for
Information Science, 38: 228-233.
Swanson D. R. (1988), “Migraine and magnesium: Eleven neglected
connections”, Perspectives in Biology and Medicine, 31(4): 526-557.
Swanson D. R. (l989a), “A second example of mutually-isolated
medical literatures related by implicit unnoticed connections”, Journal
of the American Society for Information Science, 40: 432-435.
Swanson D. R. (l989b), “Online search for logically related
non-interactive medical literatures: A systematic trial and error
strategy”, Journal of the American Society for Information Science, 40:
356-358.
Swanson D. R. (1990a), “Somatomedin C and arginine: Implicit
connections between mutuallyisolated literatures”, Perspectives in
Biology and Medicine, 33(2): 157-186.
Swanson D. R. (1990b), “The absence of co-citation as a clue to
undiscovered causal connections”, In CL. Borgman (Ed.), Scholarly
communication and bibliometrics (Ch. 7, pp. 129-137), Newbury Park,
CA: Sage.
86
Swanson D.R. (1990c), “Medical Literature as a Potential Source of
New Knowledge”, Bulletin of the Medical Library Association, 78:
29-37.
Swanson D. R. (1991), “Complementary structures in disjoint science
literatures”, Proceedings of the 14th annual international ACM SIGIR
conference on Research and development in information retrieval,
p280-289, Chicago, Illinois, United States
Swanson D. R. (1993), “Intervening in the Life Cycles of Scientific
Knowledge”, Library Trends, 41(4): 606-631.
Swanson D. R. & Smalheiser N. R. (1997), “An interactive system for
finding complementary literatures: a stimulus to scientific discovery”
Artificial Intelligence 91: 183-203.
Swanson D. R. & Smalheiser N. R. (1999), “Implicit text linkages
between Medline records: Using Arrowsmith as an aid to scientific
discovery”, Library Trends, 48(l): 48-59.
Swanson D. R., Smalheiser N. R. & Bookstein A. (2001), ”Information
discovery from complementary literatures: categorizing viruses as
potential weapons”, Journal of the American Society for Information
Science 52: 797-812.
Tan A. H. (1999), “Text Mining: The State of the Art and Challenges”,
Proc. Of the Workshop knowledge Discovery from Advanced
Databases PAKDDD-99, April
Tatro D. S. (2004), “Drug Interaction Facts”, Facts and Comparison®
A Wolters KluwerCompany 111 West Port Plaza, Suite 300 St. Louis,
Missouri 63146-3098. http: www.drugfacts.com
Thompson P.D., Clarkson P. & Karas R.H. (2003),”Statin-Associated
Myopathy”, JAMA 289(13): 1681-1690.
Van den Berg, J. & Schuemie M. (1999), “Information Retrieval
Systems using an Associative Conceptual Space”, In Proceedings 7th
European Symposium on Artificial Neural Networks (ESANN''99),
87
Bruges, Belgium.
Weeber M., Klein H., Aronson A. R., Mork J. G., Berg L., & Vos R.
(2000), “Text-based discovery in biomedicine: the architecture of the
DAD-system”, In Proceedings of AMIA, the Annual Conference of the
American Medical Informatics Association, November 4-8, 2000,
903-907.
Weeber M., Klein H., Berg L., & Vos R. (2001), “Using concepts in
literature-based discovery: Simulating Swanson’s Raynaud-Fish Oil
and Migraine-Magnesium discoveries”, Journal of the American
Society for Information Science, 52(7), 548-557.
Weeber M., Vos R., Klein H., Berg L., Aronson A. & Molema G. (2003),
“Generating Hypotheses by Discovering Implicit Associations in the
Literature: A Case Report of a Search for New Potential Therapeutic
Uses for Thalidomide”, Journal of the American Medical Informatics
Association 10(3): 252-259.