Automatic classification of adverse event text fragments

Abstract

A method, a system, and a computer program product are provided. A training set of adverse event text fragments assigned to medical codes is analyzed to determine first text fragments having frequently occurring medical code assignments and second text fragments having infrequently occurring medical code assignments. The training set is modified to undersample the first text fragments and to oversample the second text fragments such that the text fragments of the modified training set correspond to a substantially uniform assignment of the medical codes. At least one machine learning model is generated and trained with the modified training set. Some parameters of the at least one machine learning model are updated based on errors detected during the training. After completing the training, an adverse event text fragment is applied to the at least one machine learning model to assign at least one medical code.