A framework of hybrid recommender system for personalized clinical prescription

Zhang, Q; Zhang, G; Lu, J; Wu, D

A framework of hybrid recommender system for personalized clinical prescription

Zhang, Q

Zhang, G

Lu, J

Wu, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - The 2015 10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015, 2016, pp. 189 - 195
Issue Date:: 2016-01-13

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Field	Value	Language
dc.contributor.author	Zhang, Q https://orcid.org/0000-0001-9977-8418	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Wu, D https://orcid.org/0000-0002-2499-5159	en_US
dc.date.issued	2016-01-13	en_US
dc.identifier.citation	Proceedings - The 2015 10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015, 2016, pp. 189 - 195	en_US
dc.identifier.isbn	9781467393225	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43761
dc.description.abstract	© 2015 IEEE. General practitioners are faced with a great challenge of clinical prescription owing to the increase of new drugs and their complex functions to different diseases. A personalized recommender system can help practitioners discover mass of medical knowledge hidden in history medical records to deal with information overload problem in prescription. To support practitioner's decision making in prescription, this paper proposes a framework of a hybrid recommender system which integrates artificial neural network and case-based reasoning. Three issues are considered in this system framework: (1) to define a patient's need by giving his/her symptom, (2) to mine features from free text in medical records and (3) to analyze temporal efficiency of drugs. The proposed recommender system is expected to help general practitioners to improve their efficiency and reduce risks of making errors in daily clinical consultation with patients.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP110103733
dc.relation.ispartof	Proceedings - The 2015 10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015	en_US
dc.relation.isbasedon	10.1109/ISKE.2015.98	en_US
dc.title	A framework of hybrid recommender system for personalized clinical prescription	en_US
dc.type	Conference Proceeding
utslib.for	080105 Expert Systems	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 IEEE. General practitioners are faced with a great challenge of clinical prescription owing to the increase of new drugs and their complex functions to different diseases. A personalized recommender system can help practitioners discover mass of medical knowledge hidden in history medical records to deal with information overload problem in prescription. To support practitioner's decision making in prescription, this paper proposes a framework of a hybrid recommender system which integrates artificial neural network and case-based reasoning. Three issues are considered in this system framework: (1) to define a patient's need by giving his/her symptom, (2) to mine features from free text in medical records and (3) to analyze temporal efficiency of drugs. The proposed recommender system is expected to help general practitioners to improve their efficiency and reduce risks of making errors in daily clinical consultation with patients.

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http://hdl.handle.net/10453/43761