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 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...

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Bibliographic Details
Main Authors: Jian-Wu Xu, Shipeng Yu, Jinbo Bi, Lita, L.V., Niculescu, R.S., Bharat Rao, R.
Format: Conference Proceeding
Language:English
Subjects:
Cardiac disease
Cardiovascular diseases
Hafnium
Insurance
Medical diagnosis
Medical diagnostic imaging
Medical services
Personnel
Support vector machines
Training data
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Summary: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).
DOI:10.1109/ICMLA.2007.32