The week's pick
Random Articles
Reseach Article
Indexing Medical Images based on Collaborative Experts Reports
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 70 - Number 5 |
| Year of Publication: 2013 |
| Authors: Abir Messaoudi, Riadh Bouslimi, Jalel Akaichi |
10.5120/11955-7787
|
Abir Messaoudi, Riadh Bouslimi, Jalel Akaichi . Indexing Medical Images based on Collaborative Experts Reports. International Journal of Computer Applications. 70, 5 ( May 2013), 1-9. DOI=10.5120/11955-7787
Abstract
A patient is often willing to quickly get, from his physician, reliable analysis and concise explanation according to provided linked medical images. The fact of making choices individually by the patient's physician may lead to malpractices and consequently generates unforeseeable damages. The Institute of Medicine of the National Sciences Academy(IMNAS) in USA published a study estimating that up to 98,000 hospital deathseach year can be attributed to medical malpractice [1]. Moreover, physician, in charge of medical image analysis, might be unavailable at the right time, which may complicate the patient's state. The goal of this paper is to provide to physicians and patients, a social network that permits to foster cooperation and to overcome the problem of unavailability of doctors on site any time. Therefore, patients can submit their medical images to be diagnosed and commented by several experts instantly. Consequently, the need to process opinions and to extract information automatically from the proposed social network became a necessity due to the huge number of comments expressing specialist's reviews. For this reason, we propose a kind of comments' summary keywords-based method which extracts the major current terms and relevant words existing on physicians' annotations. The extracted keywords will present a new and robust method for image indexation. In fact, significant extracted terms will be used later to index images in order to facilitate their discovery for any appropriate use. To overcome this challenge, we propose our Terminology Extraction of Annotation (TEA) mixed approach which focuses on algorithms mainly based on statistical methods and on external semantic resources.
References
- Legal publications medical malpractice statistics in Usa (2013), http://www. base-juridique. com/statistiques-de-faute-professionnelle-medicale-en-u-s-a. htmlDing.
- Williams J. and Weber-Jahnke J. (2010). Addressing Regulatory Gaps with Privacy-by-Design. Eighth Annual International Conference on Privacy, Security and Trust, IEEE Social Networks for Health Care.
- Franklin V. , Greene A. , Waller A. , Greene S. and C. Pagliari C. (2008), Patients Engagement With Sweet Talk- A Text Messaging Support System for Young People With Diabetes. J Med Internet Res, vol. 10, no. 2.
- Grenier C. (2003), The Role of Intermediate Subject to understand the structuring of an organizational network of actors and technology - Case of a care network. Proceedings of the 9th Conference of the AIM (Association Information and Management), Grenoble.
- Marion A. and Omotayo O. (2011), Development of a Social Networking Site with a Networked Library and Conference Chat, Journal of Emerging Trends in Computing and Information Sciences.
- Bruyere C. (2004), Medico-social networks and the coordination of knowledge. Mediterranean University (Aix-Marseille II), DEA in Management Sciences; Logistics and Organization .
- Richman J. (2012), Improving healthcare through digital technology. Social Media Wiki.
- Karbasi S. (2007), Terms weighting in information retrieval; weighting model based on the ranking of terms in documents, University Paul Sabatier, PhD thesis.
- Maisonnasse L. , Gaussier E. , Chevallet J-P (2009), The combination of semantic analysis for searching medical information, conference INFORSID'2009, Toulouse.
- Gaussier E. , Maisonnasse L. and Chevallet J-P (2008),Multiplying concept sources for graph modeling, In CLEF 2007, LNCS 5152 proceedings.
- Lacoste C. , Chevallet J-P , Lim J-H , Wei X (2006),Ipal knowledge-based medical image retrieval in imageclefmed 2006, In Working Notes for the CLEF 2006 Workshop, 20-22 September, Alicante, Spain.
- Neil S. , Velte T. , Jie H. , and Clement Y. (2007),Knowledge intensive conceptual retrieval and passage extraction of biomedical literature, 30th annual international ACM SIGIR conference on Research and development in information retrieval.
- Hofmann. T. (1999), Probabilistic latent semantic indexing. Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval - SIGTR, NewYork, USA: ACM Press.
- Blei. D, Ng. A, and Jordan. M. (2003), Latent Dirichlet Allocation. Journal of Machine Learning Research, vol. 3, 2003, pp. 993-1022.
- Rijsbergen. C. (1979), Information retrieval; Butterworths. PhD thesis .
- Salton. G andMcGill. M. (1983), Introduction to Modern Information Retrieval. McGraw-Hill. Thesis, New York, 1983.
- Frakes and al, (2003) Strength and similarity of affix removal stemming algorithms. SIGIR Forum, vol. 37, pp. 26-30
- Manning. C, Raghavan. P and Hinrich Schütze. H (2008),Introduction to Information Retrieval, Cambridge University Press book.
- Church. K-W. and Hanks. P. (1990),Word association norms, mutual information and lexicography». Computational Linguistic, vol 1, Mars 1990, pp: 22-29.
- Rosa CETRO (2010), Processing tools and specialized language corpus: the example of Unitex, PhD thesis, Paris.
- Farida Aoughlis (2010), Construction of an electronic dictionary of computer terminology and automatic text analysis by local grammars, PhD thesis, Paris.
Index Terms
Keywords