LOD Cloud Mining for Prognosis Model(Case Study: Native App for Drug Recommender System)
Affiliation(s)
Department of Information Technology, Indian Institute of Information Technology (IIIT-A), Allahabad, India.
Department of Information Technology, Indian Institute of Information Technology (IIIT-A), Allahabad, India.
ABSTRACT
The goal of this project is to use the Semantic Web Technologies and Data Mining for disease diagnosis to assist health care professionals regarding the possible medication and drug to prescribe (Drug recommendation) according to the features of the patient. Numerous Decision Support Systems (DSS) and Expert Systems allow medical collaboration, like in the differential diagnosis specific or general. But, a medical recommendation system using both Semantic Web technologies and Data mining has not yet been developed which initiated this work. However, it should be mentioned that there are several system references about medicine or active ingredient interactions, but their final goal is not the Drug recommendation which uses above technologies. With this project we try to provide an assistant to the doctor for better recommendations. The patient will also able to use this system for explanation of drugs, food interaction and side effects of corresponding drugs.
Cite this paper
Kushwaha, N. , Goyal, R. , Goel, P. , Singla, S. and Vyas, O. (2014) LOD Cloud Mining for Prognosis Model(Case Study: Native App for Drug Recommender System). Advances in Internet of Things, 4, 20-28. doi: 10.4236/ait.2014.43004.
Kushwaha, N. , Goyal, R. , Goel, P. , Singla, S. and Vyas, O. (2014) LOD Cloud Mining for Prognosis Model(Case Study: Native App for Drug Recommender System). Advances in Internet of Things, 4, 20-28. doi: 10.4236/ait.2014.43004.
References
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http://datahub.io/dataset/fu-berlin-sider/resource/e84dd6f3-f22e-4d4d-9ee8-e5b004eb654c [5] Jentzsch, A. (2013) Drugbank.
http://datahub.io/dataset/fu-berlin-Drugbank/resource/8fc23108-81d0-45f2-81ec-22ea41485f49 [6] Williams, W. and Mancini, L. (2011) ICD-10 White Paper: Data Impact Across the Enterprise. The Kiran Consortium Group LLC, 1-7. [7] Sheth, A. (2005) Semantic Web & Semantic Web Services: Applications in Healthcare and Scientific Research. Proceedings of IFIP Working Conference on Industrial Applications of Semantic Web, 188. [8] Morrell, T. and Kerschberg, L. (2012) Personal Health Explorer: A Semantic Health Recommendation System. IEEE 28th International Conference on Data Engineering Workshops (ICDEW), Arlington, 1-5 April 2012, 55-59.
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http://dx.doi.org/10.1145/2254129.2254168 [11] Bizer, C., Heath, T. and Lee, T.B. (2009) Linked Data—The Story So Far. International Journals Semantic Web Information System, 3, 1-22. [12] Prudhommeaux, E. and Seaborne, A. (2008) Sparql Query Language for RDF, W3C Recommendation. [13] Marshall, M.S., Boyce, R. and Deus, H.F., et al. (2012) Emerging Practices for Mapping and Linking Life Sciences Data Using RDF—A Case Series. Web Semantics: Science, Services and Agents on the World Wide Web, 14, 2-13.
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http://dx.doi.org/10.1109/TKDE.2005.99 [17] Wolstencroft, K., Brass, A., Horrocks, I., Lord, P.W., Salter, U., Turi, D. and Stevens, R. (2005) A Little Semantic Web Goes a Long Way in Biology. Proceedings of ISWC2005, 786-800. [18] Rector, A. and Rogers, J. (2006) Ontological and Practical Issues in Using a Description Logic to Represent Medical Concept Systems: Experience from GALEN, Reasoning Web, Second Int Summer School, Tutorial Lecture 06, 4126, 197-231. [19] Ruttenberg, A., Rees, J. and Luciano, J. (2005) Experience Using OWL DL for the Exchange of Biological Pathway Information. Proceedings of the 1st OWL Experiences and Directions Workshop, Galway, 11-12 November 2005, 1-12. [20] Aranguren, M., Fernandez-Breis, J. and Dumontier, M. (2013) Special Issue on Linked Data for Health Care and the Life Science. Department of Biology, Carleton University, iOS Press. [21] Paulheim, H. (2012) Explain-a-LOD: Using Linked Open Data for Interpreting Statistics. In: Duarte, C., Carrico, L., Jorge, J.A., Oviatt, S.L. and Goncalves, D., Eds., IUI’12 Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces, ACM, New York, 313-314. [22] Beuscart, R., McNair, P. and Brender, J. (2009) Patient Safety through Intelligent Procedures in Education: The PSIP Project. Studies in Health Technology and Informatics, 148, 6-13. [23] Han, J. and Kamber, M. (2000) Data Mining: Concepts and Techniques. Morgan Kaunn Publishers Inc., San Francisco. [24] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. (2009) The WEKA Data Mining Software: An Update, SIGKDD Explorations, 11. [25] Hess, S., Kiefer, F., Carbon, R. and Maier, A. (2013) mConcAppt—A Method for the Conception of Mobile Business Applications. LNCS, Social Informatics and Telecommunications Engineering, Vol. 110, 1-20. [26] Broekstra, J., Kampman, A. and Harmelen, F. (2002) Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema. ISWC, 2342, 54-68.
[1] (2014) Sustainment Guide for Adverse Drug Events, Partnership for Patients Compaingn, Healthcare Government. [2] Chen, B., Ding, Y. and Wang, H. (2010) Chem2Bio2RDF: A Linked Open Data Portal for Systems Chemical Biology. Proceedings of 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WIIAT), Toronto, 31 August-3 September 2010, 232-239.
http://dx.doi.org/10.1109/WI-IAT.2010.183 [3] Ceusters, W., Capolupo, M., Moor, D. and Devlies, J. (2008) Introducing Realist Ontology for the Representation of Adverse Events. Proceedings of the 2008 Conference on Formal Ontology in Information Systems (FOIS 2008), 237-250. [4] Jentzsch, A. (2013) Sider.
http://datahub.io/dataset/fu-berlin-sider/resource/e84dd6f3-f22e-4d4d-9ee8-e5b004eb654c [5] Jentzsch, A. (2013) Drugbank.
http://datahub.io/dataset/fu-berlin-Drugbank/resource/8fc23108-81d0-45f2-81ec-22ea41485f49 [6] Williams, W. and Mancini, L. (2011) ICD-10 White Paper: Data Impact Across the Enterprise. The Kiran Consortium Group LLC, 1-7. [7] Sheth, A. (2005) Semantic Web & Semantic Web Services: Applications in Healthcare and Scientific Research. Proceedings of IFIP Working Conference on Industrial Applications of Semantic Web, 188. [8] Morrell, T. and Kerschberg, L. (2012) Personal Health Explorer: A Semantic Health Recommendation System. IEEE 28th International Conference on Data Engineering Workshops (ICDEW), Arlington, 1-5 April 2012, 55-59.
http://dx.doi.org/10.1109/ICDEW.2012.64 [9] Gómez-Pérez, A. and Suárez-Figueroa, M.C. (2009) NeOn Methodology for Building Ontology Networks: A Scenario-Based Methodology. Proceedings of the International Conference on Software, Services & Semantic Technology, Sofia, 28-29 October 2009. [10] Paulheim, H. and Furnkranz, J. (2012) Unsupervised Generation of Data Mining Features from Linked Open Data. Proceedings of Web Intelligence, Mining and Semantics, WIMS’12, Craiova, 13-15 June 2012.
http://dx.doi.org/10.1145/2254129.2254168 [11] Bizer, C., Heath, T. and Lee, T.B. (2009) Linked Data—The Story So Far. International Journals Semantic Web Information System, 3, 1-22. [12] Prudhommeaux, E. and Seaborne, A. (2008) Sparql Query Language for RDF, W3C Recommendation. [13] Marshall, M.S., Boyce, R. and Deus, H.F., et al. (2012) Emerging Practices for Mapping and Linking Life Sciences Data Using RDF—A Case Series. Web Semantics: Science, Services and Agents on the World Wide Web, 14, 2-13.
http://dx.doi.org/10.1016/j.websem.2012.02.003 [14] Bauer, F. and Kaltenbock, M. (2011) Linked Open Data: The Essential. Proceedings of Finite Element Analysis and CAD, Peking University Press, Beijing, 9-15. [15] Middleton, E., Shadbolt, N. and Shadbolt, D.C. (2004) Ontological User Profiling in Recommender Systems. Proceeding of ACM Transactions on Information Systems (TOIS), 22, 54-88. [16] Adomavicius, G. and Tuzhilin, A. (2005) Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. Proceedings of IEEE Transactions on Knowledge and Data Engineering, 17, 734-749.
http://dx.doi.org/10.1109/TKDE.2005.99 [17] Wolstencroft, K., Brass, A., Horrocks, I., Lord, P.W., Salter, U., Turi, D. and Stevens, R. (2005) A Little Semantic Web Goes a Long Way in Biology. Proceedings of ISWC2005, 786-800. [18] Rector, A. and Rogers, J. (2006) Ontological and Practical Issues in Using a Description Logic to Represent Medical Concept Systems: Experience from GALEN, Reasoning Web, Second Int Summer School, Tutorial Lecture 06, 4126, 197-231. [19] Ruttenberg, A., Rees, J. and Luciano, J. (2005) Experience Using OWL DL for the Exchange of Biological Pathway Information. Proceedings of the 1st OWL Experiences and Directions Workshop, Galway, 11-12 November 2005, 1-12. [20] Aranguren, M., Fernandez-Breis, J. and Dumontier, M. (2013) Special Issue on Linked Data for Health Care and the Life Science. Department of Biology, Carleton University, iOS Press. [21] Paulheim, H. (2012) Explain-a-LOD: Using Linked Open Data for Interpreting Statistics. In: Duarte, C., Carrico, L., Jorge, J.A., Oviatt, S.L. and Goncalves, D., Eds., IUI’12 Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces, ACM, New York, 313-314. [22] Beuscart, R., McNair, P. and Brender, J. (2009) Patient Safety through Intelligent Procedures in Education: The PSIP Project. Studies in Health Technology and Informatics, 148, 6-13. [23] Han, J. and Kamber, M. (2000) Data Mining: Concepts and Techniques. Morgan Kaunn Publishers Inc., San Francisco. [24] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. (2009) The WEKA Data Mining Software: An Update, SIGKDD Explorations, 11. [25] Hess, S., Kiefer, F., Carbon, R. and Maier, A. (2013) mConcAppt—A Method for the Conception of Mobile Business Applications. LNCS, Social Informatics and Telecommunications Engineering, Vol. 110, 1-20. [26] Broekstra, J., Kampman, A. and Harmelen, F. (2002) Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema. ISWC, 2342, 54-68.