Abstract: A method is presented for assigning billing codes for medical procedures. For each string in an input record describing a medical procedure in the input text description, comparing the string to entries in a dictionary of common misspelling and, in response to the string matching an entry in the dictionary, replacing the string with proper spelling; for each string in the input record, comparing the string to entries in another dictionary of abbreviations and, in response to the string matching an entry in the dictionary, replacing the string with expanded text for the abbreviation; constructing a feature vector by extracting features from the input record; for each billing code in a listing of possible billing codes, computing a classifier score for the feature vector using machine learning; and assigning a billing code to the input record from the listing of possible billing codes based on the classifier scores.

Abstract: A method is presented for generating a data set from a database. The method involves iterative data manipulation that stochastically identifies candidate entries from the cases (subjects, participants) and variables (data elements) and subsequently selects, nullifies, and imputes the information. This process heavily relies on statistical multivariate imputation to preserve the joint distributions of the complex structured data archive. At each step, the algorithm generates a complete dataset that in aggregate closely resembles the intrinsic characteristics of the original data set, however, on an individual level the rows of data are substantially altered. This procedure drastically reduces the risk for subject reidentification by stratification, as meta-data for all subjects is repeatedly and lossily encoded.

Abstract: A method is presented for assigning billing codes for medical procedures. For each string in an input record describing a medical procedure in the input text description, comparing the string to entries in a dictionary of common misspelling and, in response to the string matching an entry in the dictionary, replacing the string with proper spelling; for each string in the input record, comparing the string to entries in another dictionary of abbreviations and, in response to the string matching an entry in the dictionary, replacing the string with expanded text for the abbreviation; constructing a feature vector by extracting features from the input record; for each billing code in a listing of possible billing codes, computing a classifier score for the feature vector using machine learning; and assigning a billing code to the input record from the listing of possible billing codes based on the classifier scores.

Abstract: A method is presented for generating a data set from a database. The method involves iterative data manipulation that stochastically identifies candidate entries from the cases (subjects, participants) and variables (data elements) and subsequently selects, nullifies, and imputes the information. This process heavily relies on statistical multivariate imputation to preserve the joint distributions of the complex structured data archive. At each step, the algorithm generates a complete dataset that in aggregate closely resembles the intrinsic characteristics of the original data set, however, on an individual level the rows of data are substantially altered. This procedure drastically reduces the risk for subject reidentification by stratification, as meta-data for all subjects is repeatedly and lossily encoded.