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In this paper, we apply weighted ridge regression to tackle the highly unbalanced data issue in automatic large-scale ICD-9 coding of medical patient records. Since most of the ICD-9 codes are unevenly represented in the medical records, a weighted scheme is employed to balance positive and negative examples. The weights turn out to be associated with the instance priors from a probabilistic interpretation, and an efficient EM algorithm is developed to automatically update both the weights and the regularization parameter. Experiments on a large-scale real patient database suggest that the weighted ridge regression outperforms the conventional ridge regression and linear support vector machines (SVM).
Date of Conference: 13-15 Dec. 2007