  Extracting information from electronic health records (EHR) is a challenging
task since it requires prior knowledge of the reports and some natural language
processing algorithm (NLP). With the growing number of EHR implementations,
such knowledge is increasingly challenging to obtain in an efficient manner. We
address this challenge by proposing a novel methodology to analyze large sets
of EHRs using a modified Count Sketch data streaming algorithm termed DreamNLP.
By using DreamNLP, we generate a dictionary of frequently occurring terms or
heavy hitters in the EHRs using low computational memory compared to
conventional counting approach other NLP programs use. We demonstrate the
extraction of the most important breast diagnosis features from the EHRs in a
set of patients that underwent breast imaging. Based on the analysis,
extraction of these terms would be useful for defining important features for
downstream tasks such as machine learning for precision medicine.
