Progress Notes Classification and Keyword Extraction using Attention based Deep Learning Models with BERT
TABLE I: Clinical Progress Notes in the Categories
['Category', 'Document No.']
The data set used in this study was extracted from a large academic medical center’s EHR system. In total, there are 3981 clinical progress notes extracted. These progress notes belong to 12 different categories. The progress notes are ‘free text’ documents with many different sections written in them, such as ‘date’, ‘patient name’,‘gender’, ‘age’, ‘medication’, ‘allergies’, ‘history of present illness’ and so on. In this research, we use ‘history of present illness’ section, which contains the most information as ‘free text’ to demonstrate our system. The length of the ‘history of the present illness’ section based on the number of words varies. However, the majority of the document contains less than 300 words for that section. Since the base BERT model can only process documents with less than 512 tokens, we choose the documents with less than 250 words for this research.