A framework of hybrid recommender system for personalized clinical prescription

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2015), 2015, pp. 189 - 195
Issue Date:
2015-11-28
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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 deal with mass of medical knowledge hidden in history medical records. 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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