A hidden Markov model-based text classification of medical documents

• Published in: Journal of information science Vol. 35; no. 1; pp. 67 - 81
• Main Authors: Yi, Kwan, Beheshti, Jamshid
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.02.2009; Sage Publications; Bowker-Saur Ltd
• Subjects: Automatic classification; Classification; Content analysis; Data processing; Exact sciences and technology; Finite element method; Indexing. Classification. Abstracting; Indexing. Classification. Abstracting. Syntheses; Information and communication sciences; Information and document structure and analysis; Information processing and retrieval; Information science. Documentation; Machine learning; Markov analysis; Markov chains; Mathematical models; Medical Subject Headings-MeSH; MeSH; Sciences and techniques of general use; Text categorization; UMLS; HMM; MeSH; text classification; hidden Markov model; Evaluation; Classification; Information processing; Markov model
• ISSN: 0165-5515; 1741-6485
• DOI: 10.1177/0165551508092257

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.