Modeling a semantic recommender system for medical prescriptions and drug interaction detection

authors:

avatar Aliasghar Saghaei , * , avatar Sayyed Saeid Safaei

Koomesh: Vol.22, issue 1; 145-154
published online: August 03, 2020
article type: Research Article
received: January 01, 2018
accepted: August 03, 2019

how to cite: Saghaei A, Safaei S S. Modeling a semantic recommender system for medical prescriptions and drug interaction detection. koomesh. 2020;22(1):e153161. 

Abstract

Introduction: The administration of appropriate drugs to patients is one of the most important processes of treatment and requires careful decision-making based-on the current conditions of the patient and its history and symptoms. In many cases, patients may require more than one drug, or in addition to having a previous illness and receiving the drug, they need new drugs for the new illness, which may increase medical errors in the administration of the drug and the adverse drug events(ADE) such as drug interactions for the patient. Materials and Methods: In this article, the stages of designing and describing the requirements and the modeling of the ontology-based semantic recommender system of the prescribing physician and the discovery of the ADEs were presented. First, the requirements of the system were extracted and described in detail and then, based on the extracted requirements, the modeling of the system using the Unified Modeling Language of UML2.0 was discussed. Then, according to the extracted requirements for the discovery of ADEs, a proper ontology was designed for the system and implemented by Protégé software. In order to evaluate the functions of recommendation and discovering ADEs (interactions), a prototype was developed using Java language, and a collection of rules for reasoning and discovering interactions and ADEs were gathered. Results: The results of the system performance evaluation for the functions of detecting ADEs and medication recommendation suggests improvement of the proposed approach to 9.25% and 11.3% in the precision criterion, 29% and 60.6% in the recall, and 26% (respectively, approaches to the detection of ADEs and drug recommendations). Conclusion: The use of this system as a computerized physician ordering entry can, in addition to helping physicians to prescribe a more accurate prescription, reduce the risks to the health of patients resulting from medical errors in the prescribing phase.

References

  • 1.

    Bates DW, Boyle DL, Vander Vliet MB, Schneider J, Leape L. Relationship between medication errors and adverse drug events. J Gen Intern Med 1995; 10: 199-205.

  • 2.

    Kass-Bartelmes BL. Reducing and preventing adverse drug events to decrease hospital costs. Res Action 2001; 1: 01-0020.

  • 3.

    Samwald M, Jentzsch A, Bouton C, Kallese CS, Willighagen E, Hajagos J, et al. Linked open drug data for pharmaceutical research and development. J Cheminform 2011; 3: 19.

  • 4.

    Kushwaha N, Goyal R, Goel P, Singla S, Vyas OP. LOD cloud mining for prognosis model (case study: native app for drug recommender system). Adv Int Things 2014; 4: 20.

  • 5.

    Wishart DS, Knox C, Guo A, Shrivastava S, Hassanali M, Stothard P, Woolsey J. DrugBank: A comprehensive resource for in silico drug discovery and explorat. Nucleic Acids Res 2006; 34: D668-72.

  • 6.

    Callahan A, Cruz-Toledo J, Ansell P, Dumontier M, editors. Bio2RDF release 2: improved coverage, interoperability and provenance of life science linked data. Extend Semantic Web Confer 2013.

  • 7.

    Maedche A, Staab S. Ontology learning. Handbook on ontologies 2004; p: 173-190.

  • 8.

    Chen RC, Huang YH, Bau CT, Chen SM. A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection. Exp Syst with Appl 2012; 39: 3995-4006.

  • 9.

    Lu J, Wu D, Mao M, Wang W, Zhang G. Recommender system application developments: a survey. Decision Support Syst 2015; 74: 12-32.

  • 10.

    Ricci F, Rokach L, Shapira B, Kantor PB. Recommender systems handbook. Springer 2015.

  • 11.

    Gomez-Perez A, Fernndez-Lpez M, Corcho O. Ontological engineering: with examples from the areas of knowledge management, e-Commerce and the semantic Web: Springer Sci Business Media 2006.

  • 12.

    Wiesner M, Pfeifer D. Health recommender systems: concepts, requirements, technical basics and challenges. Int J Environ Res Public Health 2014; 11: 2580-2607.

  • 13.

    Pathak J, Kiefer RC, Chute CG. Using linked data for mining drug-drug interactions in electronic health records. Stud Health Technol Inform 2013; 192: 682-686.

  • 14.

    Sung TY, Hung FH, Chiu HW, editors. Implementation of an integrated drug information system for inpatients to reduce medication errors in administrating stage. Engineering in Medicine and Biology Society, 2008 EMBS 2008 30th Annual International Conference of the IEEE; 2008; IEEE.

  • 15.

    umbaugh, James, Ivar Jacobson, and Grady Booch. Unified modeling language reference manual, the. Pearson Higher Education, 2004.

  • 16.

    Seidl M, Scholz M, Huemer C, Kappel G. UML@ classroom: An introduction to object-oriented modeling: Springer; 2015.

  • 17.

    Hariri N, Babalhavaeji F, Farzandipour M, Nadi Ravandi S. Evaluation criteria of information retrieval systems: What we know and what we do not know. Iran J Inform Proces Manag 2014; 30: 199-221. (Persian).##.