Discrimination of Inconsistencies in Medical Data

Missing and erroneous values in patient cases can significantly impact the ability to perform biomedical research for identifying risk factors and causes of clinical events and disease progression. We present a framework that classifies the inconsistencies in a database to automatically process the data for research purposes. The goal is to improve the quality of medical databases by identifying the reason that data are missing, and automatically processing outliers and typographical errors to retain more information from a database. This framework presents an alternative to deleting cases with outliers and typographical errors, while also imputing relevant values into cases with missing data based on the reason that the data is missing, the source of the data, and the data type.