Contextual hybrid-based recommendation of Pubmed articles

  • Abdeldjalil KHELASSI Informatic Department, Sciences Faculty, University of Tlemcen, Algeria
  • Mohammed Ilyas Tchenar Knowledge and information Engineering Research Team, University of Tlemcen Algeria.
  • Youssouf Rahali Knowledge and information Engineering Research Team, University of Tlemcen Algeria
Keywords: Information filtering, context, recommendation, hybrid-based recommendation, medical record system, cardiology.

Abstract

Background: The amount of information in the medical field has been growing day by day. Also new medical articles about the preoccupied disease are published each day and the updated information is very required by physicians. The appropriate information in the appropriate moment is the goal of this research work.

Methods: Our goal is to recommend documents deemed relevant to doctors regarding the context of using a management application for electronic medical records. The principle is to extract the context of this usage: illness, Age ..., searching in the contents of documents and taking into account the rate of vote documents. For experiment and evaluation, we have used 100 articles randomly selected from pubmed about cardiology. In addition, we have developed a system that extracts the context of medical record system at the moment of exploration. The extracted context is used with users rating by the recommender system to select and rank the recommended articles for physicians in the same moment of use.

Results: The first result of this research work is the smart interaction between users and the software system by introducing the context of use. In addition, another important result is the reuse of user’s appreciation for more dynamicity and intelligibility.

Conclusion: The developed system offers the physician an appropriate recommendation of selected pubmed articles. The developed system augments the relevancy of the recommendation by analyzing the contents of articles and introducing a collaborative method.

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Author Biographies

Abdeldjalil KHELASSI, Informatic Department, Sciences Faculty, University of Tlemcen, Algeria

Dr. Eng Abdeldjalil Khelassi (Corresponding author) is an associate professor of Informatics at Abou Bekr Belkaid University of Tlemcen. Head of Knowledge and Information Engineering Research Team. Associate editor at Electronic Physician.

Mohammed Ilyas Tchenar, Knowledge and information Engineering Research Team, University of Tlemcen Algeria.

Mr.Mohammed Ilyas TCHENAR received the Master degree in Knowledge and information systems engineering from faculty of Science, Abou Bekr Belkaid university of Tlemcen, Algeria, in 2014. He is currently pursuing the PhD degree in Spatial Information Engineering and remote sensing at the School of Computer Science and Engineering, Beihang University, Beijing. His research interests include pattern recognition, remote sensing applications, change detection, computer vision, and image processing.

Youssouf Rahali, Knowledge and information Engineering Research Team, University of Tlemcen Algeria

Mr. Youssouf RAHALI is a computer engineer, he graduates from faculty of Science, Abou Bekr Belkaid university of Tlemcen, Algeria, in 2014 with a master’s degree in knowledge and information systems engineering. He works today as a web developer.

References

1. Khelassi, A. (2016, January). An augmented pragmatics by explanation-aware and recommendation-aware in the context of decision support. In Proceedings of the International Conference on Information and Knowledge Engineering (IKE) (p. 79). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
2. Baldauf, M., Dustdar, S., & Rosenberg, F. (2007). A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing, 2(4), 263-277. https://doi.org/10.1504/IJAHUC.2007.014070
3. Khelassi, A. (2015). RAMHeR: Reuse And Mining Health2. 0 Resources. Electronic Physician, 7(1), 969. https://dx.doi.org/10.14661/2F2015.969-970 PMid:26052406 PMCid:PMC4455296
4. Ramos, J. (2003, December). Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning.
Published
2017-05-05
Section
Medical technologies