Curation of a Cardiology Interface Terminology for Highlighting Electronic Health Records using Machine Learning
Researchers have developed a machine learning technique to create a Cardiology Interface Terminology (CIT) for better highlighting of details within electronic health records (EHRs). This method involves a three-phase process, starting with the derivation of training data from existing cardiology terms and EHRs. A machine learning model is then trained on this data to identify and extract further concepts, ultimately producing a final CIT that can highlight crucial information in cardiology patient notes. The system achieved a coverage of 74.21% and an average completeness of 98.2% on an unseen dataset. AI
IMPACT This approach could improve the efficiency and accuracy of clinical data analysis by automating the extraction of key information from medical records.