Automatic medical coding of patient records via weighted ridge regression

In this paper, we apply weighted ridge regression to tackle the highly unbalanced data issue in automatic largescale 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).

• Type: Conference
• Year: 2007
• Where: ICMLA
• Authors: Jian-Wu Xu, Shipeng Yu, Jinbo Bi, Lucian Vlad Lita, Radu Stefan Niculescu, R. Bharat Rao
• Keywords: Automatic Largescale Icd-9, Conventional Ridge Regression, ICMLA 2007, Machine Learning, Weighted Ridge Regression