Investigation of biological databases is not straight forward and generically needs identification of Medical Document Classification (MDC) based on hierarchy and relationship between different strata (ontogeny). Thus, MDC remains as a challenging effort. Popularly, earlier text classification has applied flat classifier. However, our research aims to show the text classification in which we opt to assess the hierarchical organization of classes or categories. In order to fulfill the aim of our research, we are considering the human disease hierarchical structure of human disease ontology with the help of simple relation from biomedical text abstracts and the ontology learning. We conducted experiments to evaluate the effects of different representations by measuring the change in classification performance with MEDLINE documents from the OHSUMED dataset. This research suggest a hierarchical classification method employing the hierarchical concept structure for classifying biomedical text abstracts by using Hidden Markov Model method (HMM). Present study demonstrates how a large number of biomedical articles are divided into quite a few subgroups in a hierarchy describing ontogeny.

 

Published In : IJCSN Journal Volume 3, Issue 4

Date of Publication : August 2014

Pages : 215 - 219

Figures : 03

Tables : --

Publication Link : Medical Document Classification from OHSUMED Dataset

 

 

 

Hira Lal Gope : Lecturer, Dept. of CSE, Sylhet Agricultural University, Sylhet-3100, Bangladesh

Pranajit Kumar Das : Lecturer, Dept. of CSE, Sylhet Agricultural University, Sylhet-3100, Bangladesh

Dr. Mohammed Jahirul Islam : Associate Professor, Dept. of CSE, Shahjalal University of Science and Technology Sylhet-3100, Bangladesh

Md. Hanif Seddiqui : Associate Professor, Dept. of CSE, University of Chittagong, Chittagong-4331, Bangladesh

 

 

 

 

 

 

 

Biomedical articles

HMM

Hierarchical classifier

Human diseases

Medline documents

Ontology learning

Here we are working on ontology-based hierarchical classification to evaluate the performance of biomedical text abstract classification. One of the reasons for this performance increase was that we used an NLP tool that was designed purely for medical phrase identification and a medical knowledge-base in our bag-of-phrases representation.

 

 

 

 

 

 

 

 

 

[1] Binti Dollah, Rozilawati, Md. Hanif Seddiqui, and Masaki Aono. "The effect of using hierarchical structure for classifying biomedical text abstracts."

[2] F. M. Couto, B. Martins and M. J. Silva, Classifying biological articles using web sources, Proceedings of the 2004 ACM symposium on Applied Computing, pp. 111-115 (2004).

[3] T. Li, S. Zhu and M. Ogihara, Hierarchical document classification using automatically generated hierarchy, Journal of Intelligent Information Systems, Springer Netherlands, Vol.29, No.2, pp. 211-230 (2007).

[4] Singh, Amrish, and Keiichi Nakata. "Hierarchical classification of web search results using personalized ontologies." Proceedings of the 3rd International Conference on Universal Access in Human-Computer Interaction, HCI International. Vol. 2005. 2005.

[5] SEBASTIANI, F. (2002). Machine learning in automated text categorization. ACM computing surveys (CSUR), 34, 1-47.

[6] Dumais, S., Platt, J., Heckerman, D., & Sahami, M. (1998, November). Inductive learning algorithms and representations for text categorization. In Proceedings of the seventh international conference on Information and knowledge management (pp. 148-155). ACM.

[7] Lewis, D. D., Schapire, R. E., Callan, J. P., & Papka, R. (1996, August). Training algorithms for linear text classifiers. In Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 298-306). ACM.

[8] Couto, Francisco M., Bruno Martins, and Mário J. Silva. "Classifying biological articles using web resources." Proceedings of the 2004 ACM symposium on Applied computing. ACM, 2004.

[9] Lewis, David D. (1992, June). An evaluation of phrasal and clustered representations on a text categorization task. In Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 37-50). ACM.

[10] Mao, Wenlei, and Wesley W. Chu. "Free-text medical document retrieval via phrase-based vector space model." Proceedings of the AMIA Symposium. American Medical Informatics Association, 2002.

[11] Wilcox, Adam, George Hripcsak, and Carol Friedman. "Using knowledge sources to improve classification of medical text reports." KDD-2000. 2000.

[12] UmutTosun HIDDEN MARKOV MODELS TO ANALYZE USER BEHAVIOUR IN NETWORK TRAFFIC Bilkent University 06800 Bilkent, Ankara, Turkey.

[13] Biological Data Working Group Federal Geographic Data Committee and USGS Biological Resources Division.