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A hidden Markov model-based text classification of medical documents

School of Library and Information Science, University of Kentucky, USA, kwan.yi{at}uky.edu

Jamshid Beheshti

School of Information Studies, McGill University, Montreal, Canada

The purpose of the study is to test the application of the hidden Markov model (HMM) using prior knowledge in medical text classification (TC). HMM has been applied to a wide range of applications in information processing, but not so much in TC applications. The Medical Subject Heading (MeSH) is utilized for prior knowledge in the model. A prototype for an HMM-based TC model is designed, and an experimental model based on the prototype is implemented so as to categorize medical documents into MeSH. A subset of OHSUMED is used for the experiments. Our results show that the performance of our model is comparable to those reported in the literature.

Key Words: hidden Markov model • HMM • MeSH • text classification • UMLS

This version was published on February 1, 2009

Journal of Information Science, Vol. 35, No. 1, 67-81 (2009)
DOI: 10.1177/0165551508092257


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