An application of least squares fit mapping to clinical classification.
This paper describes a unique approach, "Least Square Fit Mapping," to clinical data classification. We use large collections of human-assigned text-to-category matches as training sets to compute the correlations between physicians' terms and canonical concepts. A Linear Least Squares Fit (LLSF) technique is employed to obtain a mapping function which optimally fits the known matches given in a training set and probabilistically captures the unknown matches for arbitrary texts. We tested our method with 16,032 texts from the Mayo Clinic, and judged the results using human-assigned answers. In a test for comparison, the LLSF mapping achieved a precision rate of 89% at 100% recall, outperforming alternative approaches including string matching (36% precision), string matching enhanced by morphological parsing (51% precision), and statistical weighting (61% precision).
Yang
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Section of Medical Information Resources, Mayo Clinic/Foundation, Rochester, Minnesota 55905.
Chute
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D016247:Information Storage and Retrieval; D016018:Least-Squares Analysis; D008432:Mathematical Computing; D013358:Subject Headings