An application of Expert Network to clinical classification and MEDLINE indexing.
An effective and efficient learning method, Expert Network (ExpNet), is introduced in this paper. ExpNet predicts the related categories of an arbitrary text based on a search of its nearest neighbors in a set of training texts, and a reasoning from the expert-assigned categories of these neighbors. Evaluations in patient-record text classification and MEDLINE document indexing show a performance of ExpNet in recall and precision comparable to the Linear Least Squares Fit (LLSF) mapping method, and significantly better than other methods tested. We also observed that ExpNet is much more efficient than LLSF in computation. The total training and testing time on the patient-record text collection (6134 texts) was 4 minutes for ExpNet versus 96 minutes for LLSF; on the MEDLINE document collection (2344 documents), the total time was 15 minutes for ExpNet versus 4.6 hours for LLSF. It is evident in this study that human knowledge of text categorization can be statistically learned without expensive computation, and that ExpNet is such a solution.
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Section of Medical Information Resources, Mayo Clinic/Foundation, Rochester, Minnesota 55905.
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D000043:Abstracting and Indexing / Q000379:methods* / Q000706:statistics & numerical data; D005069:Evaluation Studies as Topic; D005103:Expert Systems; D006801:Humans; D016018:Least-Squares Analysis; D016239:MEDLINE; D008499:Medical Records / Q000145:classification*; D016571:Neural Networks, Computer; D013358:Subject Headings; D014825:Vocabulary