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<meta content="Bindoff, Ivan Karl" name="eprints.creators_name" />
<meta content="Tenni, Peter" name="eprints.creators_name" />
<meta content="Kang, Byeong Ho" name="eprints.creators_name" />
<meta content="Peterson, Gregory M." name="eprints.creators_name" />
<meta content="ibindoff@utas.edu.au" name="eprints.creators_id" />
<meta content="Peter.Tenni@utas.edu.au" name="eprints.creators_id" />
<meta content="bhkang@utas.edu.au" name="eprints.creators_id" />
<meta content="G.Peterson@utas.edu.au" name="eprints.creators_id" />
<meta content="Hoffman, Achim" name="eprints.editors_name" />
<meta content="Kang, Byeong Ho" name="eprints.editors_name" />
<meta content="Richards, Debbie" name="eprints.editors_name" />
<meta content="Tsumoto, Shusaku" name="eprints.editors_name" />
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<meta content="2007-01-24" name="eprints.datestamp" />
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<meta content="Intelligent Decision Support for Medication Review" name="eprints.title" />
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<meta content="artficial intelligence, knowledge acquisition, medication review, adverse drug events, RDR, MCRDR" name="eprints.keywords" />
<meta content="This paper examines an implementation of a Multiple Classification Ripple Down Rules system which can be used to provide quality Decision Support Services to pharmacists practicing medication reviews (MRs), particularly for high risk patients. The system was trained on 84 genuine cases by an expert in the field; over the course of 15 hours the system had learned 197 rules and was considered to encompass around 60% of the domain. Furthermore, the system was found able to improve the quality and consistency of the medication review reports produced, as it was shown that there was a high incidence of missed classifications under normal conditions, which were repaired by the system automatically." name="eprints.abstract" />
<meta content="2006-08" name="eprints.date" />
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<meta content="Guilin, China" name="eprints.place_of_pub" />
<meta content="120-131" name="eprints.pagerange" />
<meta content="10.1007/11961239_11" name="eprints.id_number" />
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<meta content="1. Peterson, G., Continuing evidence of inappropriate medication usage in the elderly,
in Australian Pharmacist. 2004. p. 2.
2. Bates, D., et al., Incidence of adverse drug events and potential adverse drug
events. Implications for prevention. ADE Prevention Study Group. JAMA,
1995(274): p. 29-34.
3. Peterson, G., The future is now: the importance of medication review, in Australian
Pharmacist. 2002. p. 268-75.
4. Rigby, D., The challenge of change - establishing an HMR service in the pharmacy,
in Australian Pharmacist. 2004. p. 214-217.
5. Kinrade, W., Review of Domiciliary Medication Management Review Software.
2003, Pharmacy Guild of Australia. p. 77.
6. Aamodt, A. and E. Plaza, Case-Based Reasoning: Foundational Issues, Methodological
Variations, and System Approaches, in AICom - Artificial Intelligence
Communications. 1994. p. 39-59.
7. Compton, P., et al. Knowledge Acquisition without Analysis. in Knowledge Acquisition
for Knowledge-Based Systems. 1993. Springer Verlag.
8. Tenni, P., et al. to I. Bindoff. 2005.
9. Bonner, C., MediFlags. 2005.
10. Compton, P. and R. Jansen. A philosophical basis for knowledge acquisition. in
European Knowledge Acquisition for Knowledge-Based Systems. 1989. Paris.
11. Rivest, R., Learning Decision Lists, in Machine Learning. 1987. p. 229-246.
12. Kang, B., P. Compton, and P. Preston, Multiple Classification Ripple Down Rules.
1994.
13. Kolodner, J., R. Simpson, and K. Sycara-Cyranski. A Process Model of Casedbased
Reasoning in Problem Solving. in International Joint Conference on Artificial
Intelligence. 1985. Los Angeles: Morgan Kaufmann.
14. Kolodner, J.L., Special Issue on Case-Based Reasoning - Introduction. Machine
Learning, 1993. 10(3): p. 195-199.
15. Kang, B. and P. Compton, A Maintenance Approach to Case Based Reasoning.
1994.
16. Kang, B., P. Compton, and P. Preston. Multiple Classification Ripple Down Rules:
Evaluation and Possibilities. in AIII-Sponsored Banff Knowledge Acquisition for
Knowledge-Based Systems. 1995. Banff.
17. Bindoff, I., An Intelligent Decision Support System for Medication Review, in
Computing. 2005, University of Tasmania: Hobart. p. 65.
18. Preston, P., G. Edwards, and P. Compton. A 2000 Rule Expert System Without a
Knowledge Engineer. in AIII-Sponsored Banff Knowledge Acquisition for Knowledge-
Based Systems. 1994. Banff.
19. Compton, P. and R. Jansen. Cognitive aspects of knowledge acquisition. in AAAI
Spring Consortium. 1992. Stanford.
20. Tenni, P. to I. Bindoff. 2005." name="eprints.referencetext" />
<meta content="Bindoff, Ivan Karl and Tenni, Peter and Kang, Byeong Ho and Peterson, Gregory M. (2006) Intelligent Decision Support for Medication Review. In: Advances in Knowledge Acquisition and Management. Lecture Notes in Artificial Intelligence, 4303 . Springer, Guilin, China, pp. 120-131. ISBN 3540689559" name="eprints.citation" />
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<meta content="Tenni, Peter" name="DC.creator" />
<meta content="Kang, Byeong Ho" name="DC.creator" />
<meta content="Peterson, Gregory M." name="DC.creator" />
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<meta content="320500 Pharmacology and Pharmaceutical Sciences" name="DC.subject" />
<meta content="This paper examines an implementation of a Multiple Classification Ripple Down Rules system which can be used to provide quality Decision Support Services to pharmacists practicing medication reviews (MRs), particularly for high risk patients. The system was trained on 84 genuine cases by an expert in the field; over the course of 15 hours the system had learned 197 rules and was considered to encompass around 60% of the domain. Furthermore, the system was found able to improve the quality and consistency of the medication review reports produced, as it was shown that there was a high incidence of missed classifications under normal conditions, which were repaired by the system automatically." name="DC.description" />
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<h1 class="ep_tm_pagetitle">Intelligent Decision Support for Medication Review</h1>
<p style="margin-bottom: 1em" class="not_ep_block"><span class="person_name">Bindoff, Ivan Karl</span> and <span class="person_name">Tenni, Peter</span> and <span class="person_name">Kang, Byeong Ho</span> and <span class="person_name">Peterson, Gregory M.</span> (2006) <xhtml:em>Intelligent Decision Support for Medication Review.</xhtml:em> In: Advances in Knowledge Acquisition and Management. Lecture Notes in Artificial Intelligence, 4303 . Springer, Guilin, China, pp. 120-131. ISBN 3540689559</p><p style="margin-bottom: 1em" class="not_ep_block"></p><table style="margin-bottom: 1em" class="not_ep_block"><tr><td valign="top" style="text-align:center"><a onmouseover="EPJS_ShowPreview( event, 'doc_preview_647' );" href="http://eprints.utas.edu.au/636/1/IDSS4MR-PKAW-sr.pdf" onmouseout="EPJS_HidePreview( event, 'doc_preview_647' );"><img alt="[img]" src="http://eprints.utas.edu.au/style/images/fileicons/application_pdf.png" class="ep_doc_icon" border="0" /></a><div class="ep_preview" id="doc_preview_647"><table><tr><td><img alt="" src="http://eprints.utas.edu.au/636/thumbnails/1/preview.png" class="ep_preview_image" border="0" /><div class="ep_preview_title">Preview</div></td></tr></table></div></td><td valign="top"><a href="http://eprints.utas.edu.au/636/1/IDSS4MR-PKAW-sr.pdf"><span class="ep_document_citation">PDF (Author Version)</span></a> - Requires a PDF viewer<br />108Kb</td></tr></table><p style="margin-bottom: 1em" class="not_ep_block">Official URL: <a href="http://www.springerlink.com/content/978-3-540-68955-3/">http://www.springerlink.com/content/978-3-540-68955-3/</a></p><div class="not_ep_block"><h2>Abstract</h2><p style="padding-bottom: 16px; text-align: left; margin: 1em auto 0em auto">This paper examines an implementation of a Multiple Classification Ripple Down Rules system which can be used to provide quality Decision Support Services to pharmacists practicing medication reviews (MRs), particularly for high risk patients. The system was trained on 84 genuine cases by an expert in the field; over the course of 15 hours the system had learned 197 rules and was considered to encompass around 60% of the domain. Furthermore, the system was found able to improve the quality and consistency of the medication review reports produced, as it was shown that there was a high incidence of missed classifications under normal conditions, which were repaired by the system automatically.</p></div><table style="margin-bottom: 1em" cellpadding="3" class="not_ep_block" border="0"><tr><th valign="top" class="ep_row">Item Type:</th><td valign="top" class="ep_row">Book Chapter</td></tr><tr><th valign="top" class="ep_row">Keywords:</th><td valign="top" class="ep_row">artficial intelligence, knowledge acquisition, medication review, adverse drug events, RDR, MCRDR</td></tr><tr><th valign="top" class="ep_row">Subjects:</th><td valign="top" class="ep_row"><a href="http://eprints.utas.edu.au/view/subjects/280201.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280201 Expert Systems</a><br /><a href="http://eprints.utas.edu.au/view/subjects/320500.html">320000 Medical and Health Sciences > 320500 Pharmacology and Pharmaceutical Sciences</a></td></tr><tr><th valign="top" class="ep_row">ID Code:</th><td valign="top" class="ep_row">636</td></tr><tr><th valign="top" class="ep_row">Deposited By:</th><td valign="top" class="ep_row"><span class="ep_name_citation"><span class="person_name">Mr Ivan Bindoff</span></span></td></tr><tr><th valign="top" class="ep_row">Deposited On:</th><td valign="top" class="ep_row">24 Jan 2007</td></tr><tr><th valign="top" class="ep_row">Last Modified:</th><td valign="top" class="ep_row">09 Jan 2008 02:30</td></tr><tr><th valign="top" class="ep_row">ePrint Statistics:</th><td valign="top" class="ep_row"><a target="ePrintStats" href="/es/index.php?action=show_detail_eprint;id=636;">View statistics for this ePrint</a></td></tr></table><p align="right">Repository Staff Only: <a href="http://eprints.utas.edu.au/cgi/users/home?screen=EPrint::View&eprintid=636">item control page</a></p>
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